Triple
T13195277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claire Tow Theater |
E314094
|
entity |
| Predicate | hasTicketModel |
P108991
|
FINISHED |
| Object | low-priced tickets for LCT3 productions |
—
|
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: low-priced tickets for LCT3 productions | Statement: [Claire Tow Theater, hasTicketModel, low-priced tickets for LCT3 productions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTicketModel Context triple: [Claire Tow Theater, hasTicketModel, low-priced tickets for LCT3 productions]
-
A.
hasTicketIntegration
Indicates that there is an established connection enabling ticket-related data or actions to be shared or synchronized between systems or components.
-
B.
hasTicketAccess
Indicates that an entity is permitted to view, use, or manage a particular ticket or set of tickets.
-
C.
hasTicketFormat
Indicates that an entity’s ticket is expressed or structured in a particular format.
-
D.
hasTicketing
Indicates that an entity provides or is associated with a system or mechanism for issuing, managing, or selling tickets.
-
E.
hasTicketBarrier
Indicates that an access-controlled barrier or gate is present, typically requiring a valid ticket or pass to pass through.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc6bc108190b5a6a265bf6e9fd4 |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98ceeb22c8190a6be666031d9e5a4 |
completed | April 10, 2026, 11:51 p.m. |
Created at: April 9, 2026, 9:16 p.m.