Triple
T37300938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houston–Dallas |
E925937
|
entity |
| Predicate | hasTravelMarket |
P48370
|
FINISHED |
| Object | business travel |
—
|
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: business travel | Statement: [Houston–Dallas, hasTravelMarket, business travel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTravelMarket Context triple: [Houston–Dallas, hasTravelMarket, business travel]
-
A.
hasTravelNetwork
Indicates that one entity possesses, manages, or is connected through a system or infrastructure enabling travel or transportation between locations.
-
B.
hasTravelType
Indicates the mode or category of travel associated with a given trip, journey, or movement event.
-
C.
travelMarket
chosen
Indicates a relationship where an entity participates in or is associated with the commercial exchange, promotion, or sale of travel-related services or experiences.
-
D.
hasTourismMarketing
Indicates that an entity engages in or is associated with activities, strategies, or efforts aimed at promoting tourism.
-
E.
traveledIn
Indicates that an entity moved or journeyed using a particular vehicle, medium, 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_69f76eb1bc508190924e9fa5d8acdeb3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a001adc8c108190ab3a43f6415e2be3 |
completed | May 10, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_6a001a290330819097c2c3123f9014b4 |
completed | May 10, 2026, 5:39 a.m. |
Created at: May 3, 2026, 4:16 p.m.