Triple
T8727368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kita-Senju |
E207162
|
entity |
| Predicate | hasCommercialFacility |
P12416
|
FINISHED |
| Object | department stores |
—
|
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: department stores | Statement: [Kita-Senju, hasCommercialFacility, department stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialFacility Context triple: [Kita-Senju, hasCommercialFacility, department stores]
-
A.
hasCommercialInfrastructure
Indicates that an entity possesses or is equipped with facilities, systems, or structures that support commercial or business activities.
-
B.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
C.
hasFacilities
chosen
Indicates that an entity possesses, provides, or is equipped with certain facilities or physical resources.
-
D.
hasCivicAmenity
Indicates that an entity possesses, provides, or is associated with a public facility or service intended for community use.
-
E.
hasShoppingMall
Indicates that one entity possesses, contains, or includes a shopping mall within its area or domain.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d16cba881908e2a14b60ae65524 |
completed | March 31, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69cc457093188190959287a6458651c6 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:37 p.m.