Triple
T806079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trump National Golf Club |
E17435
|
entity |
| Predicate | hasTargetCustomer |
P481
|
FINISHED |
| Object | affluent golfers |
—
|
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: affluent golfers | Statement: [Trump National Golf Club, hasTargetCustomer, affluent golfers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTargetCustomer Context triple: [Trump National Golf Club, hasTargetCustomer, affluent golfers]
-
A.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
B.
hasTargetCity
Indicates that something is directed toward, intended for, or specifically associated with a particular city as its target.
-
C.
customerType
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
D.
hasMarketParticipants
Indicates that a market or trading venue involves or is associated with specific participating entities (such as buyers, sellers, or intermediaries).
-
E.
targetMarket
chosen
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ace495348190aec66f35ea90bc89 |
completed | March 1, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69a4aa70973c8190adbf08302d1103a9 |
completed | March 1, 2026, 9:06 p.m. |
Created at: March 1, 2026, 7:38 p.m.