Triple
T2847559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Green Park Underground station |
E63016
|
entity |
| Predicate | hasTicketHalls |
P27344
|
FINISHED |
| Object | multiple ticket halls |
—
|
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: multiple ticket halls | Statement: [Green Park Underground station, hasTicketHalls, multiple ticket halls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTicketHalls Context triple: [Green Park Underground station, hasTicketHalls, multiple ticket halls]
-
A.
hasTicketHall
chosen
Indicates that a place or facility includes or is equipped with a designated ticket hall area for purchasing or validating tickets.
-
B.
numberOfHalls
Indicates the quantity of halls associated with a given entity or location.
-
C.
hasAuditorium
Indicates that one entity possesses or includes an auditorium as part of its facilities.
-
D.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
E.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given entity.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf3ebbac819090e2bf98ed9fbd02 |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd0e86808190bcefffafbd3cd441 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.