Triple
T17875928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Street–Sheridan Square |
E446951
|
entity |
| Predicate | hasTokenBooth |
P41089
|
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: [Christopher Street–Sheridan Square, hasTokenBooth, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTokenBooth Context triple: [Christopher Street–Sheridan Square, hasTokenBooth, yes]
-
A.
hasTicketBooths
Indicates that one entity possesses or contains ticket booths used for selling or distributing tickets.
-
B.
hasSecurityTerminal
Indicates that an entity is equipped with or contains a security terminal used for access control, monitoring, or security-related operations.
-
C.
hasSelfServiceTicketMachines
Indicates that an entity is equipped with self-service ticket machines available for use.
-
D.
hasVIPTerminal
Indicates that one entity possesses or provides access to a VIP (very important person) terminal associated with another entity.
-
E.
hasCollectorBooth
chosen
Indicates that an entity has an associated collector booth, typically a designated place or station where collection-related activities (such as payments, tickets, or items) are handled.
- 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49aa614b48190bdc9e905e9e6d5e0 |
completed | April 19, 2026, 9:04 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e6d2e88190ad9ef9f8a99f13e6 |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:18 a.m.