Triple
T1036300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Francisco 4th and King Street station |
E22369
|
entity |
| Predicate | hasDropOffArea |
P24210
|
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: [San Francisco 4th and King Street station, hasDropOffArea, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDropOffArea Context triple: [San Francisco 4th and King Street station, hasDropOffArea, yes]
-
A.
dropOffOptions
Indicates the available ways, locations, or conditions under which something can be dropped off or delivered.
-
B.
dropOffOption
Indicates an available method or arrangement by which something can be left or delivered at a specified location.
-
C.
hasStopArea
Indicates that an entity is associated with or contains a specific stop area, such as a designated location where vehicles stop.
-
D.
hasWaitingArea
Indicates that an entity provides or includes a designated space where people can wait before receiving a service or proceeding to another area.
-
E.
hasCargoTerminal
Indicates that a location or facility includes or is equipped with a cargo terminal for handling freight.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b97c64a88190bf1119fdd4940bf3 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b729f8488190b2042bd9c625a833 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b97acbf4819087b92a8b29baef46 |
completed | March 1, 2026, 10:11 p.m. |
Created at: March 1, 2026, 7:41 p.m.