Triple
T27399184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sugamo |
E691781
|
entity |
| Predicate | hasShoppingStreetLength |
P169254
|
FINISHED |
| Object | approximately 800 meters |
—
|
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: approximately 800 meters | Statement: [Sugamo, hasShoppingStreetLength, approximately 800 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShoppingStreetLength Context triple: [Sugamo, hasShoppingStreetLength, approximately 800 meters]
-
A.
isShoppingStreet
Indicates that a location functions primarily as a street characterized by a concentration of shops and commercial retail activity.
-
B.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
C.
hasShoppingDistrictName
Indicates that an entity’s shopping district is identified by a specific name.
-
D.
hasShoppingDistrictType
Indicates that an entity is associated with a particular type or category of shopping district.
-
E.
hasNumberOfStreets
Indicates the relationship that specifies how many streets are associated with or contained within a given entity.
- F. None of above. chosen
Provenance (4 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_69ef5204f7048190bf226a129858fc5b |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f67d691a948190afa7fb19ae7d4ac5 |
completed | May 2, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f67c9ec1708190b26ccf402ed7b106 |
completed | May 2, 2026, 10:37 p.m. |
Created at: April 27, 2026, 12:28 p.m.