Triple
T6389705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kameoka |
E143790
|
entity |
| Predicate | neighboringCity |
P988
|
FINISHED |
| Object | Nagaokakyo |
E135579
|
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: Nagaokakyo | Statement: [Kameoka, neighboringCity, Nagaokakyo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagaokakyo Context triple: [Kameoka, neighboringCity, Nagaokakyo]
-
A.
Nagaokakyo
chosen
Nagaokakyo is a suburban city in Japan known for its bamboo groves, historical temples, and convenient location between Kyoto and Osaka.
-
B.
Kamogawa
Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
-
C.
Kamogawa
Kamogawa is a prominent river running through Kyoto, Japan, known for its scenic banks, cultural significance, and popular walking paths.
-
D.
Kizugawa
Kizugawa is a city in southern Kyoto Prefecture, Japan, known for its mix of historical sites, residential areas, and growing industrial and research facilities.
-
E.
Futakotamagawa
Futakotamagawa is a riverside commercial and residential district in Tokyo known for its large shopping complexes, upscale housing, and scenic Tama River views.
- 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_69c7487f26048190aeed34af6f0a8387 |
completed | March 28, 2026, 3:18 a.m. |
Created at: March 22, 2026, 4:34 p.m.