Triple
T95598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rhône River |
E1922
|
entity |
| Predicate | cityOnRiver |
P165
|
FINISHED |
| Object | Avignon |
E28595
|
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: Avignon | Statement: [Rhône River, cityOnRiver, Avignon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Avignon Context triple: [Rhône River, cityOnRiver, Avignon]
-
A.
Avignon
chosen
Avignon is a historic city in southeastern France renowned for its medieval architecture, including the Palais des Papes, and its role as a former seat of the papacy.
-
B.
Arles
Arles is a historic city in southern France renowned for its well-preserved Roman monuments and its association with the painter Vincent van Gogh.
-
C.
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.
-
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.
Marseille
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a256a7957c8190bf9924eff7572b95 |
completed | Feb. 28, 2026, 2:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a35e9e2b8c8190913c083e841c98d8 |
completed | Feb. 28, 2026, 9:31 p.m. |
Created at: Feb. 28, 2026, 2:09 a.m.