Triple
T14574751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Takaishi |
E342012
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object | Sakai |
E8407
|
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: Sakai | Statement: [Takaishi, neighboringMunicipality, Sakai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sakai Context triple: [Takaishi, neighboringMunicipality, Sakai]
-
A.
Sakai
chosen
Sakai is a major Japanese city in Osaka Prefecture known historically as a prosperous port and merchant center and today as an important industrial and cultural hub.
-
B.
Sakai Port
Sakai Port is a historic Japanese harbor city area in Osaka Prefecture that has long served as a key commercial and maritime gateway.
-
C.
Daiko Campus
Daiko Campus is one of Nagoya University's satellite campuses in Nagoya, Japan, housing specialized faculties and research facilities.
-
D.
Kindai
Kindai is a major private university in Japan known for its comprehensive academic programs and strong research in fields such as science, engineering, and fisheries.
-
E.
Sakae
Sakae is a major downtown commercial and entertainment district in Nagoya, Japan, known for its shopping, nightlife, and landmark attractions.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f49d58819094fcd2a702e146cb |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8acc788081909c41905785fa9a29 |
completed | May 8, 2026, 7:03 a.m. |
Created at: April 10, 2026, 1:24 a.m.