Triple
T26877178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinjuku-sanchome Station (via Fukutoshin Line) |
E676783
|
entity |
| Predicate | hasFacilitiesNearby |
P5648
|
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: [Shinjuku-sanchome Station (via Fukutoshin Line), hasFacilitiesNearby, department stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFacilitiesNearby Context triple: [Shinjuku-sanchome Station (via Fukutoshin Line), hasFacilitiesNearby, department stores]
-
A.
hasNearbyFacility
chosen
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
B.
hasFacilities
Indicates that an entity possesses, provides, or is equipped with certain facilities or physical resources.
-
C.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
-
D.
operatedFacilityNear
Indicates that an entity operated a facility located in close geographic proximity to another specified entity or place.
-
E.
hasFacilityLocation
Indicates that an entity possesses or is associated with a facility situated at a specific location.
- 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_69eee9bb44988190b6e11652d028bc59 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 27, 2026, 5:36 a.m.