Triple
T9739161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University Street |
E236141
|
entity |
| Predicate | mayIntersectWith |
P50696
|
FINISHED |
| Object | campus entrances |
—
|
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: campus entrances | Statement: [University Street, mayIntersectWith, campus entrances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayIntersectWith Context triple: [University Street, mayIntersectWith, campus entrances]
-
A.
hasNotableIntersection
Indicates that two entities intersect or cross at a point that is considered significant or noteworthy in some context.
-
B.
overlapsWith
Indicates that two entities share a common part or region in space, time, or extent, but neither is completely contained within the other.
-
C.
hasCrossingPoint
chosen
Indicates that two or more entities intersect or share at least one common point in space or along their paths.
-
D.
collidesWith
Indicates that two entities come into contact with each other in space, typically implying an impact or physical intersection of their paths or volumes.
-
E.
fieldIntersection
Indicates that two or more fields or domains share a common overlapping area or set of elements.
- 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_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. |
Created at: March 30, 2026, 8:22 p.m.