Triple
T23760616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | S (Shuttle) train |
E587237
|
entity |
| Predicate | runsNearLandmark |
P61362
|
FINISHED |
| Object | Chrysler Building |
—
|
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: Chrysler Building | Statement: [S (Shuttle) train, runsNearLandmark, Chrysler Building]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runsNearLandmark Context triple: [S (Shuttle) train, runsNearLandmark, Chrysler Building]
-
A.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
B.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
C.
hasFormerNearbyLandmark
Indicates that an entity previously had a nearby landmark that no longer exists or no longer holds the same status or relevance.
-
D.
southernNearbyLandmark
Indicates that one landmark is located to the south and in close proximity to another landmark.
-
E.
nearbyTo
chosen
Indicates that one entity is located close in distance or position to another entity.
- 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_69e2490b8ac48190a6b35f1d5500486b |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bdb289508190a4a1049762102794 |
completed | April 29, 2026, 8:13 a.m. |
| PD | Predicate disambiguation | batch_69f155f79e34819080f9ddb972b34deb |
completed | April 29, 2026, 12:51 a.m. |
Created at: April 17, 2026, 7:14 p.m.