Triple
T4700769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Love Field |
E104262
|
entity |
| Predicate | commercialService |
P58611
|
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: [Dallas Love Field, commercialService, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commercialService Context triple: [Dallas Love Field, commercialService, yes]
-
A.
commercialAccess
Indicates that an entity is permitted to use or access something for commercial or profit-generating purposes.
-
B.
commercial
Indicates that one entity is engaged in a business-related or profit-oriented relationship, activity, or transaction with another entity.
-
C.
commercialProduct
Indicates that one entity is a product or service offered for sale or commercial use by another entity.
-
D.
commercialIntroduction
Indicates that one entity introduces or connects another entity to a commercial opportunity, partner, product, or service for business purposes.
-
E.
commercialActivity
Indicates an action or relationship involving the buying, selling, or exchange of goods or services for profit or other economic gain.
- 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_69bd43e9b88481908582103dcadff3d9 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd650ad0f88190844bfcb46b3071c2 |
completed | March 20, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69bd621ba7448190a53ab1e2897acf71 |
completed | March 20, 2026, 3:04 p.m. |
| PDg | Predicate description generation | batch_69bd6508e218819086a36236cfa4a249 |
completed | March 20, 2026, 3:17 p.m. |
Created at: March 20, 2026, 1:17 p.m.