Triple
T13253574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Champs-sur-Marne |
E315596
|
entity |
| Predicate | arrondissement |
P2709
|
FINISHED |
| Object | Torcy |
E809567
|
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: Torcy | Statement: [Champs-sur-Marne, arrondissement, Torcy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Torcy Context triple: [Champs-sur-Marne, arrondissement, Torcy]
-
A.
Torcy
chosen
Torcy is a commune in France known for its twinning partnership with the German town of Sundern.
-
B.
Bourgueil
Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
-
C.
Chelles–Gournay
Chelles–Gournay is a suburban railway station in the eastern Paris metropolitan area that serves as the outer terminus of RER line E.
-
D.
Villejust
Villejust is a small commune in the Essonne department of the Île-de-France region in northern France, situated in the southwestern suburbs of Paris.
-
E.
Grigny
Grigny is a suburban commune in the southern outskirts of Paris, France, known for its large housing estates and diverse population.
- 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_69d806b1072881909e46bd212259c5f0 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98f7517048190b4eac4e44e81ff66 |
completed | April 11, 2026, 12:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01232149f88190b385fca6a7588d7b |
completed | May 11, 2026, 12:30 a.m. |
Created at: April 9, 2026, 9:24 p.m.