Triple
T9753784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jason Lee |
E236503
|
entity |
| Predicate | wasProfessionalIn |
P5863
|
FINISHED |
| Object | skateboarding |
—
|
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: skateboarding | Statement: [Jason Lee, wasProfessionalIn, skateboarding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasProfessionalIn Context triple: [Jason Lee, wasProfessionalIn, skateboarding]
-
A.
professionalWins
Indicates that one entity has achieved a certain number of victories or successes in a professional context, such as in a career, competition, or formal domain.
-
B.
professionalism
Indicates that an entity consistently behaves, communicates, and performs tasks in a manner that adheres to accepted standards of competence, ethics, and workplace conduct.
-
C.
professionalSince
Indicates the point in time when an entity began its professional activity or career in a given role or field.
-
D.
professional
chosen
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
E.
hasProfessionalReputationFor
Indicates that an entity is recognized by others as being notably associated with a particular professional quality, skill, or behavior.
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9fb01ad08190b2435fa505c622bc |
completed | April 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:24 p.m.