Triple
T20009099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Météo-France |
E494538
|
entity |
| Predicate | hasOfficeIn |
P1268
|
FINISHED |
| Object | Toulouse |
—
|
NE NERFINISHED |
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: Toulouse | Statement: [Météo-France, hasOfficeIn, Toulouse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toulouse Context triple: [Météo-France, hasOfficeIn, Toulouse]
-
A.
Toulouse
chosen
Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
-
B.
Toulouse
"Toulouse" is a popular 2011 electro house track by Dutch DJ and producer Nicky Romero that helped establish his international reputation in the EDM scene.
-
C.
Toulouse
Toulouse is a fictional orange kitten from Disney's animated film "The Aristocats," known for his playful, boisterous personality and admiration of alley cats.
-
D.
Montpellier
Montpellier is an affluent district of Cheltenham, England, known for its Regency architecture, boutique shops, and café culture.
-
E.
Montpellier
Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a81c5881909692fcaaf59a57c9 |
completed | April 20, 2026, 5:26 p.m. |
Created at: April 11, 2026, 3:33 p.m.