Triple
T146897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Trump Organization |
E3348
|
entity |
| Predicate | hasBusinessModelElement |
P3097
|
FINISHED |
| Object | licensing the Trump name |
—
|
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: licensing the Trump name | Statement: [The Trump Organization, hasBusinessModelElement, licensing the Trump name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusinessModelElement Context triple: [The Trump Organization, hasBusinessModelElement, licensing the Trump name]
-
A.
hasTradingModel
Indicates that one entity uses, is governed by, or is associated with a particular trading model.
-
B.
usesElement
chosen
Indicates that one entity makes use of, incorporates, or depends on a specified element in its structure, function, or behavior.
-
C.
hasProduct
Indicates that an entity possesses, offers, or is associated with a particular product.
-
D.
hasPart
Indicates that one entity is a component, segment, or constituent part of another entity.
-
E.
hasEconomicOrganization
Indicates that an entity possesses, is associated with, or participates in a specific economic organization or institutional economic structure.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a258808ff08190a06b6206f635612b |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256580c2c8190beecca60ca8595f3 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.