Triple
T1592195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carson Wentz |
E34201
|
entity |
| Predicate | collegePosition |
P29636
|
FINISHED |
| Object | quarterback |
—
|
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: quarterback | Statement: [Carson Wentz, collegePosition, quarterback]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: collegePosition Context triple: [Carson Wentz, collegePosition, quarterback]
-
A.
collegeEmployer
Indicates that a college or university is the employing institution of a given person or organization.
-
B.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
-
C.
college
Indicates that an entity is a college-level educational institution attended by or associated with another entity.
-
D.
stateUniversity
Indicates that an institution is a university that is publicly funded and operated under the authority of a state or similar governmental entity.
-
E.
positionInWork
Indicates the specific role, rank, or placement an entity holds within a larger work or structured composition.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a916d413f08190a4e137e5ed262e25 |
completed | March 5, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69a907bfb39c8190a31e0be14d3d52e6 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a916d2fae48190aaac6b2a5e31a7cf |
completed | March 5, 2026, 5:38 a.m. |
Created at: March 4, 2026, 7:27 p.m.