Triple
T12618382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaya Airport |
E301314
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Gaya |
E295112
|
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: Gaya | Statement: [Gaya Airport, servesCity, Gaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gaya Context triple: [Gaya Airport, servesCity, Gaya]
-
A.
Gaya
chosen
Gaya is a historic city in the Indian state of Bihar, renowned as a major Hindu and Buddhist pilgrimage center, especially for the Vishnupad Temple and its proximity to Bodh Gaya.
-
B.
Gaya
Gaya is a historic town and important urban center in northern Nigeria’s Kano State.
-
C.
Giha
Giha is an alternate name for the Ha language, a Bantu language spoken primarily in western Tanzania.
-
D.
Geisa
Geisa is a small historic town in the state of Thuringia in central Germany, near the former inner-German border.
-
E.
Aisai
Aisai is a city in central Japan known for its agricultural landscape and location within Aichi Prefecture near the Nagoya metropolitan area.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d960c63ea48190ae1aae9280a023a6 |
completed | April 10, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ed507988190b9d46586f3c3584c |
completed | May 2, 2026, 8:30 p.m. |
Created at: April 9, 2026, 5:13 p.m.