Triple
T35078880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LPFL |
E1012377
|
entity |
| Predicate | hasScheduledAirlines |
P19814
|
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: [LPFL, hasScheduledAirlines, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScheduledAirlines Context triple: [LPFL, hasScheduledAirlines, yes]
-
A.
hasScheduledFlights
chosen
Indicates that there are one or more flights planned and set to occur between the related entities according to a schedule.
-
B.
wasPlannedToFlyOn
Indicates that an entity was scheduled or intended to travel on a particular flight or aircraft.
-
C.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
-
D.
airlineContext
Indicates a relationship, situation, or action that specifically occurs within or is constrained by an airline-related context (such as flights, carriers, or air travel operations).
-
E.
appliesToAirline
Indicates that something (such as a rule, policy, offer, or condition) is specifically relevant or applicable to a particular airline.
- 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_69f76dd32c008190853aef6028f60208 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: May 3, 2026, 4:01 p.m.