Triple
T16471941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Na |
E400082
|
entity |
| Predicate | associatedProfessionOfBearer |
P35215
|
FINISHED |
| Object | tennis player |
—
|
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: tennis player | Statement: [Na, associatedProfessionOfBearer, tennis player]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedProfessionOfBearer Context triple: [Na, associatedProfessionOfBearer, tennis player]
-
A.
isAssociatedWithProfessionOfBearer
chosen
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
B.
relatedProfession
Indicates that two entities have professions that are connected or associated in some meaningful way, such as being in the same field, industry, or professional domain.
-
C.
occupationOfAssociatedPerson
Indicates the job or professional role held by a person who is associated with another referenced entity.
-
D.
occupationalAssociation
Indicates a relationship where one entity is connected to another through a job, profession, or work-related role.
-
E.
associatedWithCareerOf
Indicates a relationship where something is connected or relevant to a person’s professional life, occupation, or career trajectory.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dd19df881909e4562a5e8473338 |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:11 a.m.