Triple
T76647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caltrain |
E1530
|
entity |
| Predicate | openedAsCaltrain |
P1712
|
FINISHED |
| Object | 1985 |
—
|
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: 1985 | Statement: [Caltrain, openedAsCaltrain, 1985]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: openedAsCaltrain Context triple: [Caltrain, openedAsCaltrain, 1985]
-
A.
openedAsRailStop
chosen
Indicates that an entity began operation or was first established specifically as a railway stop.
-
B.
openedAsRedLineStation
Indicates that a station began operation specifically as part of the Red Line when it first opened.
-
C.
openedIn
Indicates that an entity (such as a business, event, or institution) began operating or was inaugurated in a specific time period or location.
-
D.
servedByRailroad
Indicates that a location or facility is provided with transportation or service by a railroad line or company.
-
E.
notableTrain
Indicates that there is a train or rail service associated with the subject that is considered notable or significant in some way.
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a2559892dc81909303f2eefdc0025f |
completed | Feb. 28, 2026, 2:40 a.m. |
| PD | Predicate disambiguation | batch_69a24eaf99e481908e8d314577e22ecf |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.