Triple
T9739175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University Street |
E236141
|
entity |
| Predicate | typicalNearbyLandmarks |
P90437
|
FINISHED |
| Object | lecture halls |
—
|
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: lecture halls | Statement: [University Street, typicalNearbyLandmarks, lecture halls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNearbyLandmarks Context triple: [University Street, typicalNearbyLandmarks, lecture halls]
-
A.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
B.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
C.
notableNearbySite
Indicates that one entity is a significant or noteworthy site located close to another entity.
-
D.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
E.
nearbyTo
Indicates that one entity is located close in distance or position to another 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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ef43fec8190987628f401a27436 |
completed | April 1, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
| PDg | Predicate description generation | batch_69cd06aa8bc88190904be19c8953def8 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:22 p.m.