Triple
T31968092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nakamise-dori |
E816234
|
entity |
| Predicate | hasNumberOfShopsApprox |
P181092
|
FINISHED |
| Object | about 90 |
—
|
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: about 90 | Statement: [Nakamise-dori, hasNumberOfShopsApprox, about 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfShopsApprox Context triple: [Nakamise-dori, hasNumberOfShopsApprox, about 90]
-
A.
hasShopCount
chosen
Indicates the number of shops associated with a given entity.
-
B.
hasShopsOn
Indicates that one entity (typically a street, area, or building) contains or is lined with shops located on or along it.
-
C.
hasIndependentShops
Indicates that an entity contains or is associated with retail businesses that operate independently rather than as part of large chains or franchises.
-
D.
numberOfStores
Indicates the total count of stores associated with a given entity or context.
-
E.
hasTraditionalShops
Indicates that an entity possesses or contains shops that operate in a traditional, customary, or long-established manner.
- 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_69f348f5ae5481909da0247869f51955 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fdee770af48190aca2670db50f8b49 |
completed | May 8, 2026, 2:08 p.m. |
| PD | Predicate disambiguation | batch_69fdecec98a08190a357d816dc2a6dbe |
completed | May 8, 2026, 2:02 p.m. |
Created at: May 1, 2026, 12:10 a.m.