Triple
T3759997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ryan Bingham |
E82137
|
entity |
| Predicate | travelPattern |
P50789
|
FINISHED |
| Object | constant air travel across the United States |
—
|
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: constant air travel across the United States | Statement: [Ryan Bingham, travelPattern, constant air travel across the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelPattern Context triple: [Ryan Bingham, travelPattern, constant air travel across the United States]
-
A.
transportCorridor
Indicates a route or pathway used to move people, goods, or resources between locations.
-
B.
transportationPlanned
Indicates that an arrangement has been made for someone or something to be transported from one place to another.
-
C.
commutesBetween
Indicates a regular pattern of travel back and forth between two locations, typically for work, study, or routine activities.
-
D.
involvedTravelBetween
Indicates a relationship where an entity participates in or is associated with travel occurring between two specified locations.
-
E.
hasCommuterPattern
Indicates that there is a characteristic or recurring pattern in how an entity regularly travels between locations, typically for work or daily activities.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc3d3f48190974cec104080949f |
completed | March 8, 2026, 7:19 p.m. |
| PD | Predicate disambiguation | batch_69adc04c851c8190ae5eaebf36df539b |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc0fe3e3c8190bd886c7745c172a0 |
completed | March 8, 2026, 6:33 p.m. |
Created at: March 8, 2026, 3:35 p.m.