Triple
T7877717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Odakyu department store |
E182898
|
entity |
| Predicate | locatedInCommercialArea |
P16988
|
FINISHED |
| Object | Shinjuku commercial district |
—
|
LITERAL 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: Shinjuku commercial district | Statement: [Odakyu department store, locatedInCommercialArea, Shinjuku commercial district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInCommercialArea Context triple: [Odakyu department store, locatedInCommercialArea, Shinjuku commercial district]
-
A.
connectsToCommercialArea
Indicates that one location has a direct link, route, or access path to a commercial area.
-
B.
isInBusinessDistrict
chosen
Indicates that an entity is located within a designated business or commercial district area.
-
C.
commercialArea
Indicates that the location or region is designated primarily for commercial activities such as businesses, shops, or services.
-
D.
locatedIn
Indicates that one entity exists or is situated within the spatial, administrative, or conceptual boundaries of another entity.
-
E.
hasBusinessDistrict
Indicates that a place or administrative area contains or includes a designated business district within its boundaries.
- F. None of above.
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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39bc07208190aa452cef8ca5b0d6 |
completed | March 31, 2026, 3:04 a.m. |
| PD | Predicate disambiguation | batch_69cae928e1b88190b0620f4c4f03bc7d |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:57 p.m.