Triple
T6852315
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beach 36th Street |
E158048
|
entity |
| Predicate | hasCrossunders |
P72623
|
FINISHED |
| Object | no |
—
|
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: no | Statement: [Beach 36th Street, hasCrossunders, no]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCrossunders Context triple: [Beach 36th Street, hasCrossunders, no]
-
A.
hasCrossunder
Indicates that one entity passes or extends from one side to the other beneath another entity, forming an under-crossing relationship between them.
-
B.
hasCrossovers
Indicates that one entity features or participates in crossover appearances or interactions with another entity or set of entities.
-
C.
hasCross
Indicates that one entity possesses, displays, or is marked by a cross in relation to another entity or context.
-
D.
crossesUnder
Indicates that one entity passes beneath another entity’s path or structure, moving from one side to the other without intersecting it at the same elevation.
-
E.
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.
- 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_69c6882fae988190864cbba788c5ebb4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d84fffbc8190943ca7f3f03937e9 |
completed | March 27, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69c6d0a12834819097d7e6c0b823745e |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1668a7c8190ae93951f9ba2df10 |
completed | March 27, 2026, 6:50 p.m. |
Created at: March 27, 2026, 2:20 p.m.