Triple
T19495245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bel Air–Les Brosses |
E487751
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Bel Air–Les Brosses |
—
|
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: Bel Air–Les Brosses | Statement: [Bel Air–Les Brosses, hasName, Bel Air–Les Brosses]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bel Air–Les Brosses Context triple: [Bel Air–Les Brosses, hasName, Bel Air–Les Brosses]
-
A.
Bel Air–Les Brosses
chosen
Bel Air–Les Brosses is a tram stop in the Lyon public transport network served by line T3.
-
B.
Bressant
Bressant is a novel by American author Julian Hawthorne, known as one of his early works in 19th-century fiction.
-
C.
Belmont-Broye
Belmont-Broye is a municipality in the canton of Fribourg in western Switzerland, formed through the merger of several smaller communes.
-
D.
Fins Bois
Fins Bois is a Cognac cru in the Charente region of France known for producing round, fruity eaux-de-vie that mature relatively quickly.
-
E.
Butte-aux-Cailles
Butte-aux-Cailles is a picturesque, village-like neighborhood in Paris known for its cobbled streets, street art, and lively cafés.
- 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63490c16481908423e304d82722d7 |
completed | April 20, 2026, 2:13 p.m. |
Created at: April 10, 2026, 1:40 p.m.