diff --git a/configs/fa_ner_sm.cfg b/configs/fa_ner_sm.cfg
index 8e6f209..d32bc43 100644
--- a/configs/fa_ner_sm.cfg
+++ b/configs/fa_ner_sm.cfg
@@ -1,10 +1,11 @@
 # fa_ent_news_sm — Persian NER, CPU size (sm).
 #
-# A standalone artifact, not part of the shipping `dep` pipeline. It is trained on
-# ParsTwiNER (MIT, tweets) while the rest of the project trains on UD_Persian-PerDT
-# (CC BY-SA 4.0, edited prose), and it scores 67.22 F against 85-98 for the UD
-# components — different corpus, different genre, different quality tier, so it gets its
-# own package rather than being hidden inside a `core` one. Built by `spacy project run ner`.
+# Standalone package, built by `spacy project run ner`, sitting alongside (not inside)
+# fa_dep_news_sm. Trained on the PerDT treebank's own NER layer (CC BY-SA 4.0, edited
+# prose), transferred onto this project's tokenization by scripts/transfer_perdt_ner.py.
+# Scores ENTS_F 71.87 on the PerDT test split; see docs/MODELS.md §3.2 for the corpus
+# survey (ParsTwiNER, ARMAN, PEYMA and others were considered and rejected on licence or
+# genre grounds).
 #
 # Deliberate deviation from `spacy init config --pipeline ner`: the tok2vec is
 # EMBEDDED inside components.ner.model instead of being a separate `tok2vec`
@@ -12,8 +13,8 @@
 # it was trained in; embedding makes the component self-contained and therefore
 # sourceable into another pipeline via `nlp.add_pipe("ner", source=...)`. This is the same
 # design as en_core_web_sm, whose `ner` "has its own independent internal tok2vec"
-# (https://spacy.io/models#design). That is what will let a future fa_core_news_sm combine
-# this component (or its ../ner_dataset replacement) with the dep pipeline.
+# (https://spacy.io/models#design). `assemble-core` in project.yml uses exactly that to
+# source this component into fa_core_news_sm alongside the dep pipeline.
 
 [paths]
 train = null