Triple
T8883725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 4 (Paris Métro) |
E211472
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Mouton-Duvernet |
E199953
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mouton-Duvernet | Statement: [Line 4 (Paris Métro), hasStation, Mouton-Duvernet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mouton-Duvernet Context triple: [Line 4 (Paris Métro), hasStation, Mouton-Duvernet]
-
A.
Mouton-Duvernet
chosen
Mouton-Duvernet is a Paris Métro station in the 14th arrondissement, serving the Montparnasse area and named after the French general Régis Barthélemy Mouton-Duvernet.
-
B.
Mouton
Mouton is an academic publishing house known for its influential works in linguistics and related fields.
-
C.
Tardieu
Tardieu is a French surname historically associated with several notable figures in politics, arts, and sciences.
-
D.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
E.
Cagne
The Cagne is a small coastal river in southeastern France that flows through the town of Cagnes-sur-Mer into the Mediterranean Sea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc616b2d988190b923ef1e33aab787 |
completed | April 1, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabd254148190b5ea3d308fe96851 |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:53 p.m.