Triple
T15954899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sapporo metropolitan area |
E386908
|
entity |
| Predicate | hasSuburb |
P747
|
FINISHED |
| Object | Eniwa |
E770850
|
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: Eniwa | Statement: [Sapporo metropolitan area, hasSuburb, Eniwa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eniwa Context triple: [Sapporo metropolitan area, hasSuburb, Eniwa]
-
A.
Eniwa
chosen
Eniwa is a city in Hokkaido, Japan, known for its natural scenery, parks, and proximity to Sapporo.
-
B.
Nakawa
Nakawa is one of the energetic human hosts in Disney’s “Festival of the Lion King” stage show at Disney’s Animal Kingdom.
-
C.
Shimaore
Shimaore is a Bantu language closely related to Comorian, widely spoken by the local population of Mayotte in the Indian Ocean.
-
D.
Aobayama
Aobayama is a hilly, forested area in Sendai known for housing parts of Tohoku University and offering scenic views over the city.
-
E.
Nagareyama
Nagareyama is a city in Chiba Prefecture, Japan, known as a residential suburb of the Tokyo metropolitan area with growing commuter access and family-oriented neighborhoods.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156f94b108190905d295c3405860e |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c44fe7c81908eb32c0b0967365f |
completed | May 10, 2026, 8:05 a.m. |
Created at: April 10, 2026, 4:53 a.m.