Triple
T232169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penn Station (New York City) |
E4431
|
entity |
| Predicate | originalBuildingDemolished |
P1583
|
FINISHED |
| Object | 1963 |
—
|
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: 1963 | Statement: [Penn Station (New York City), originalBuildingDemolished, 1963]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalBuildingDemolished Context triple: [Penn Station (New York City), originalBuildingDemolished, 1963]
-
A.
buildingsDestroyed
chosen
Indicates that one or more buildings have been damaged to the point of destruction as a result of some event or action.
-
B.
building
Indicates that one entity constructs, assembles, or develops another entity, typically over a period of time.
-
C.
hasCauseOfDestruction
Indicates that one entity is the cause or agent responsible for the destruction or damage of another entity.
-
D.
significantBuilding
Indicates that a building holds notable importance, prominence, or special status within a particular context (e.g., historical, cultural, architectural, or functional).
-
E.
formerSiteOf
Indicates that a location previously hosted or contained something (such as a structure, organization, or event) that is no longer present there.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f14f72081908182e76300b59358 |
completed | Feb. 28, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69a25b5c8c888190b5544e687736b373 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.