Triple
T5014308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LYD |
E112702
|
entity |
| Predicate | codeFor |
P3746
|
FINISHED |
| Object | Lyon-Part-Dieu |
E487674
|
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: Lyon-Part-Dieu | Statement: [LYD, codeFor, Lyon-Part-Dieu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyon-Part-Dieu Context triple: [LYD, codeFor, Lyon-Part-Dieu]
-
A.
Quartier Part-Dieu
chosen
Quartier Part-Dieu is Lyon’s main business district, known for its high-rise offices, major shopping center, and one of France’s busiest railway stations.
-
B.
Villeurbanne
Villeurbanne is a major suburban city adjacent to Lyon in eastern France, known for its dense urban character and role as part of the Lyon metropolitan area.
-
C.
La Croix-Rousse
La Croix-Rousse is a historic hilltop district in Lyon, France, known for its silk-weaving heritage, steep slopes, and distinctive village-like atmosphere.
-
D.
Lyon
Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
-
E.
Boulogne-Billancourt
Boulogne-Billancourt is a densely populated suburban city just southwest of central Paris, known as a major economic and media hub in the Île-de-France region.
- 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_69bd4434acb8819086679dbeccc2fe54 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7310c5b08190a5c9ab0f9fe9569f |
completed | March 20, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec34c4b8c8190a91be145e105f129 |
completed | March 21, 2026, 4:11 p.m. |
Created at: March 20, 2026, 1:35 p.m.