Triple
T9212843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huahine |
E221167
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Fare |
E785423
|
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: Fare | Statement: [Huahine, hasSettlement, Fare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fare Context triple: [Huahine, hasSettlement, Fare]
-
A.
Fare
chosen
Fare is the main village and administrative center of the island of Huahine in French Polynesia, serving as its primary hub for commerce and transportation.
-
B.
FARE
FARE was the air force of the Spanish Republic during the Spanish Civil War, operating as the aerial branch of the Republican military.
-
C.
Taksim
Taksim is a central district and major transportation and cultural hub on the European side of Istanbul, Turkey.
-
D.
MARC fare system
The MARC fare system is the ticketing and pricing structure used by Maryland's MARC commuter rail service for travel across its network.
-
E.
flat fare (Washington Metro)
The flat fare in the Washington Metro is a simplified pricing system where riders pay a uniform fare regardless of travel distance or zones.
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda05406081909893bec3a092d3ce |
completed | April 1, 2026, 8:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0778e8dc48190bbae39137df966e3 |
completed | April 4, 2026, 2:29 a.m. |
Created at: March 30, 2026, 7:27 p.m.