Triple
T37032160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A-10 |
E916524
|
entity |
| Predicate | usedForTicketingSystems |
P38658
|
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: [A-10, usedForTicketingSystems, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForTicketingSystems Context triple: [A-10, usedForTicketingSystems, yes]
-
A.
usedForTicketType
Indicates that something is utilized or applicable for a specific type or category of ticket.
-
B.
usedByTicket
Indicates that something (such as a resource, item, or service) is utilized or referenced by a specific ticket.
-
C.
usedInE-tickets
chosen
Indicates that something (such as a method, technology, or feature) is employed or applied within the context of electronic tickets (e-tickets).
-
D.
usedInRailwayTickets
Indicates that something is employed or applied in the context of railway tickets, such as their creation, validation, or usage.
-
E.
ticketingUsage
Indicates how a ticketing system or ticket-based access is used or applied in a given context.
- 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_69f76e92c7648190bcfa277f64c71a21 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd592e48cc81909d754cc6c4bd99ae |
completed | May 8, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69fd58b7f9b881909dc099b28d567784 |
completed | May 8, 2026, 3:30 a.m. |
Created at: May 3, 2026, 4:14 p.m.