Triple
T1058133
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyoto Prefecture |
E22843
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Kyotanabe |
E141768
|
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: Kyotanabe | Statement: [Kyoto Prefecture, hasCity, Kyotanabe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kyotanabe Context triple: [Kyoto Prefecture, hasCity, Kyotanabe]
-
A.
Kyotanabe
chosen
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
-
B.
Kameoka
Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
-
C.
Kawagoe
Kawagoe is a historic Japanese city in Saitama Prefecture, often called "Little Edo" for its well-preserved Edo-period streetscapes and traditional warehouses.
-
D.
Nantan
Nantan is a city in central Kyoto Prefecture, Japan, known for its rural landscapes, forests, and traditional cultural sites.
-
E.
Maishima
Maishima is a man-made island in Osaka, Japan, known for its sports facilities, event venues, and waterfront recreational 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_69a493dada0481909c43649f9843ea91 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8dc9e8c819099fbb192bcf80615 |
completed | March 1, 2026, 10:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf01f74c8190938753276629fdf3 |
completed | March 10, 2026, 5:41 a.m. |
Created at: March 1, 2026, 7:42 p.m.