Triple
T8945560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westchester campus |
E213212
|
entity |
| Predicate | commuterFriendly |
P3916
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Westchester campus, commuterFriendly, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commuterFriendly Context triple: [Westchester campus, commuterFriendly, true]
-
A.
commuterHubFor
Indicates a location that serves as a primary transit or gathering point for commuters traveling to or from another place.
-
B.
hasCommuterOrientation
chosen
Indicates that an entity is designed or intended primarily for use by commuters, emphasizing suitability for regular travel between home and work or study.
-
C.
commuterDestination
Indicates that a location serves as the endpoint or target place to which a person regularly travels for commuting.
-
D.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
E.
commuterMarket
Indicates a market or customer segment composed primarily of people who regularly commute, typically targeted based on their commuting patterns and needs.
- 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_69ca839843408190a39069a029a89f15 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66db998c8190999a7a686bbdda1f |
completed | April 1, 2026, 12:29 a.m. |
| PD | Predicate disambiguation | batch_69cc5ed5267c8190a43feb2a2f3df1ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 6:59 p.m.