Triple
T1146007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Center Gai |
E23565
|
entity |
| Predicate | hasStreetEnvironment |
P24448
|
FINISHED |
| Object | high-density signage |
—
|
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: high-density signage | Statement: [Center Gai, hasStreetEnvironment, high-density signage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStreetEnvironment Context triple: [Center Gai, hasStreetEnvironment, high-density signage]
-
A.
hasStreet
Indicates that an entity is located on, associated with, or identified by a particular street.
-
B.
hasTreeLinedStreets
Indicates that the streets in a given area are lined or bordered with trees along their sides.
-
C.
hasNearbyStreet
Indicates that one entity is located close to or adjacent to a street.
-
D.
betweenStreets
Indicates that one location is situated between two specified streets, typically along a road segment bounded by those streets.
-
E.
pedestrianFriendly
Indicates that an environment, route, or area is designed or suitable for safe, comfortable, and convenient use by pedestrians.
- 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc6e8c2081909fb3534413b7aacb |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4d4104819084027a043c6118cb |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bbb9fb4c81909dd39c496893c21b |
completed | March 1, 2026, 10:20 p.m. |
Created at: March 1, 2026, 7:44 p.m.