Triple
T2020594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Texas–New York |
E44094
|
entity |
| Predicate | hasTravelMode |
P15183
|
FINISHED |
| Object | air travel between Texas and New York |
—
|
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: air travel between Texas and New York | Statement: [Texas–New York, hasTravelMode, air travel between Texas and New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTravelMode Context triple: [Texas–New York, hasTravelMode, air travel between Texas and New York]
-
A.
hasGroundTransportation
Indicates that an entity provides, includes, or is connected to transportation services or options that operate on land (e.g., cars, buses, trains).
-
B.
hasPublicTransportUsage
Indicates that an entity makes use of, or is associated with the use of, public transportation services.
-
C.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
D.
passesUsedForTransportation
Indicates that the passes are utilized as a means or instrument for transporting people or goods.
-
E.
travelsOn
chosen
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_69a8891201bc8190aca837be6de41579 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb8d0bcbc8190bbbb726ecae1c51b |
completed | March 7, 2026, 5:34 a.m. |
| PD | Predicate disambiguation | batch_69abb7a389408190a84a54856352f15b |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:38 p.m.