Triple
T1398245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aeroméxico |
E30718
|
entity |
| Predicate | operatesLongHaulRoutes |
P5534
|
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: [Aeroméxico, operatesLongHaulRoutes, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operatesLongHaulRoutes Context triple: [Aeroméxico, operatesLongHaulRoutes, yes]
-
A.
hasLongHaulFlights
chosen
Indicates that an entity (such as an airline, route, or airport) operates or is associated with long-distance flights typically covering intercontinental or extended-duration journeys.
-
B.
longDistanceOperator
Indicates that an entity performs telephone or communication services that connect calls over long distances between different geographic areas.
-
C.
operatesInterCityServices
Indicates that an entity runs or provides transportation services connecting different cities.
-
D.
primaryLongHaulRegion
Indicates the main long-distance geographic region in which an entity primarily operates or is associated for long-haul activities.
-
E.
travelsOn
Indicates that an entity moves or journeys using a particular route, path, or mode of transportation.
- 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_69a498fd4e408190bd73eca30ea9754c |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c382b6588190833c39ac84fb6139 |
completed | March 1, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69a4bf017f8081908572121560ec621f |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.