Triple
T6389704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kameoka |
E143790
|
entity |
| Predicate | neighboringCity |
P988
|
FINISHED |
| Object | Nantan |
E172607
|
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: Nantan | Statement: [Kameoka, neighboringCity, Nantan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nantan Context triple: [Kameoka, neighboringCity, Nantan]
-
A.
Nantan
chosen
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
-
B.
Kyotanabe
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
-
C.
Tanabe
Tanabe is a coastal city in Japan known as a gateway to the Kumano Kodo pilgrimage routes and for its scenic natural landscapes.
-
D.
Marunouchi
Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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_69c008db906c819096f3597d55d95432 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0686cc6d481909c62a29a84a4ce8e |
completed | March 22, 2026, 10:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8681d57c08190b72d1b010db3a065 |
completed | March 28, 2026, 11:45 p.m. |
Created at: March 22, 2026, 4:34 p.m.