Triple
T27329049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Henry Bridge |
E689741
|
entity |
| Predicate | crossesLocality |
P27425
|
FINISHED |
| Object | Mount Pleasant |
—
|
NE NERFINISHED |
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: Mount Pleasant | Statement: [Mount Henry Bridge, crossesLocality, Mount Pleasant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossesLocality Context triple: [Mount Henry Bridge, crossesLocality, Mount Pleasant]
-
A.
crossesInCity
Indicates that one entity crosses or passes through another entity within the boundaries of a specified city.
-
B.
crossesNear
Indicates that one entity passes across the path or area of another entity at a location that is close to, but not directly intersecting, the other entity.
-
C.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
D.
crossesInCityArea
Indicates that one entity crosses or passes through another entity specifically within an urban or city-designated area.
-
E.
crossesIn
chosen
Indicates that one entity passes over or through the path, boundary, or area occupied by another entity, intersecting its space or trajectory.
- 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_69ef355d4cb08190ab032c0a2e7d3753 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69f67c9fe7b48190b79b4041357edb49 |
completed | May 2, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69f678cc272081909e5c70f1bc7407f0 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 27, 2026, 11:37 a.m.