Triple
T232174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penn Station (New York City) |
E4431
|
entity |
| Predicate | trackCount |
P1707
|
FINISHED |
| Object | more than 20 tracks |
—
|
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: more than 20 tracks | Statement: [Penn Station (New York City), trackCount, more than 20 tracks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trackCount Context triple: [Penn Station (New York City), trackCount, more than 20 tracks]
-
A.
numberOfTracks
chosen
Indicates the quantity of tracks associated with a given entity.
-
B.
trackNumberOnAlbum
Indicates the specific position or sequence number that a track occupies on an album.
-
C.
tracks
Indicates that one entity monitors, follows, or keeps a record of another entity’s state, behavior, or progress over time.
-
D.
trackType
Indicates the specific kind or category of track associated with an entity, such as its functional or physical classification.
-
E.
hasTrack
Indicates that one entity possesses, includes, or is associated with a specific track (such as a path, course, or recorded item).
- 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.