Triple
T10037840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enterprise |
E205216
|
entity |
| Predicate | numberOfFreeFlights |
P92058
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Enterprise, numberOfFreeFlights, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFreeFlights Context triple: [Enterprise, numberOfFreeFlights, 5]
-
A.
numberOfFlights
Indicates the total count of flights associated with a given entity or within a specified context.
-
B.
hasScheduledFlights
Indicates that there are one or more flights planned and set to occur between the related entities according to a schedule.
-
C.
hasTypeOfFlights
Indicates that an entity offers, includes, or is associated with specific categories or kinds of flights.
-
D.
hasFreeTransfer
Indicates that one entity allows or provides a transfer to another service, route, or segment without additional cost.
-
E.
fareAppliesTo
Indicates that a specific fare is applicable to a particular trip, service, passenger category, or travel condition.
- 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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcede428c8190ae115dc3425f9b0e |
completed | April 2, 2026, 2:05 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8638508190b22acc65500ec7d6 |
completed | April 1, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69cd4fed19d481909d2c7ff1114664b6 |
completed | April 1, 2026, 5:03 p.m. |
Created at: March 30, 2026, 8:55 p.m.