Triple
T10297015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Setagaya |
E241517
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Sangenjaya area |
E300644
|
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: Sangenjaya area | Statement: [Setagaya, hasDistrict, Sangenjaya area]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sangenjaya area Context triple: [Setagaya, hasDistrict, Sangenjaya area]
-
A.
Toba Kakar area
The Toba Kakar area is a mountainous region in western Pakistan’s Balochistan province, forming part of the Toba Kakar Range near the Afghan border.
-
B.
Rancabali area
Rancabali area is a highland region in southern Bandung, West Java, known for its tea plantations, cool climate, and proximity to natural attractions like Kawah Putih.
-
C.
Kaya area
The Kaya area is a region in Burkina Faso known as a primary speech area of the Mooré language.
-
D.
Sangenjaya
chosen
Sangenjaya is a lively Tokyo neighborhood known for its dense network of bars, cafes, and eateries, as well as its convenient access to central Shibuya.
-
E.
Pankshin area
Pankshin area is a locality in Plateau State, central Nigeria, known for its diverse ethnic groups and use of the Angas language.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ebd258819099fadddcd13099fc |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d2cc9c48190bc36f6a4f8144b7f |
completed | April 9, 2026, 3:29 a.m. |
Created at: April 6, 2026, 11:43 a.m.