Triple
T4301176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Affrique |
E99838
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object | Rodez |
E171042
|
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: Rodez | Statement: [Saint-Affrique, nearbyCity, Rodez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rodez Context triple: [Saint-Affrique, nearbyCity, Rodez]
-
A.
Rodez
chosen
Rodez is a historic cathedral city in southern France that serves as the capital of the Aveyron department in the Occitanie region.
-
B.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
-
C.
Montpellier
Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
-
D.
Toulouse
Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
-
E.
Saint-Étienne
Saint-Étienne is an industrial city in central France known for its historic manufacturing heritage, football culture, and role as one of the host cities for major international sporting events.
- 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_69b345528ebc8190b5abc7e95094792d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3509fb2b88190a13ab88a5b924052 |
completed | March 12, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf8bbef34c8190ae4b22a94e1cf91a |
completed | March 22, 2026, 6:27 a.m. |
Created at: March 12, 2026, 11:08 p.m.