Triple
T14482880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yelena Isinbayeva |
E359153
|
entity |
| Predicate | laterSpecializedIn |
P114376
|
FINISHED |
| Object | pole vault |
—
|
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: pole vault | Statement: [Yelena Isinbayeva, laterSpecializedIn, pole vault]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterSpecializedIn Context triple: [Yelena Isinbayeva, laterSpecializedIn, pole vault]
-
A.
laterSpecialization
Indicates that one entity becomes a more specialized or refined version of another entity at a later point in time.
-
B.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
C.
portrayedAsSpecialization
Indicates that one entity is depicted or represented as a specialized or more specific version of another entity.
-
D.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
E.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de924bc548819087a2f693840d7426 |
completed | April 14, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69de5c487b4c819097803e58dca628a5 |
completed | April 14, 2026, 3:24 p.m. |
| PDg | Predicate description generation | batch_69de5fb4de14819092acdecbd201d672 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:20 a.m.