Triple
T4553025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zelníčková |
E120412
|
entity |
| Predicate | bearerRelation |
P11385
|
FINISHED |
| Object | former wife of Donald Trump |
—
|
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: former wife of Donald Trump | Statement: [Zelníčková, bearerRelation, former wife of Donald Trump]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bearerRelation Context triple: [Zelníčková, bearerRelation, former wife of Donald Trump]
-
A.
bearer
chosen
Indicates that one entity carries, holds, or possesses another entity, often as the current holder of a right, document, or object.
-
B.
supportsRelation
Indicates that one entity provides assistance, endorsement, or structural backing to another entity or its activity.
-
C.
valueRelation
Indicates a comparative or associative relationship between the values or magnitudes of two or more entities.
-
D.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
E.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd581160e08190b715a8ce5c3e6c9b |
completed | March 20, 2026, 2:22 p.m. |
| PD | Predicate disambiguation | batch_69bd5223423c81908317351b58cff5f5 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:09 p.m.