Triple
T13863117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 145th Street (IND Concourse Line) |
E333247
|
entity |
| Predicate | hasIBeamColumns |
P112161
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [145th Street (IND Concourse Line), hasIBeamColumns, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIBeamColumns Context triple: [145th Street (IND Concourse Line), hasIBeamColumns, yes]
-
A.
hasTypicalBeam
Indicates that an entity is associated with a characteristic or standard type of beam it commonly uses or possesses.
-
B.
hasBeamSpecies
Indicates a relationship where an object or structure possesses or is associated with a particular type or species of beam.
-
C.
hasFrontColumns
Indicates that one entity possesses or features columns located at its front side.
-
D.
hasBeamlines
Indicates that one entity possesses, contains, or is associated with one or more beamlines.
-
E.
hasNumberOfStandingColumns
Indicates the relationship specifying how many standing columns an entity currently has.
- F. None of above. chosen
Provenance (4 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_69d81c5ced9c8190b0e9bcc6effe5959 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a101488190bd790b28033d38b9 |
completed | April 14, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69de05972f3881909977b4c843984f88 |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239524688190a0f2408c239cfcaa |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 9, 2026, 10:14 p.m.