Triple
T1976636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 蔡崇信 |
E42929
|
entity |
| Predicate | 职业 |
P20899
|
FINISHED |
| Object | 企业家 |
—
|
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: 企业家 | Statement: [蔡崇信, 职业, 企业家]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 职业 Context triple: [蔡崇信, 职业, 企业家]
-
A.
businessCareer
chosen
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
B.
employment
Indicates a relationship where one entity hires, contracts, or otherwise engages another to perform work or services, typically in exchange for compensation.
-
C.
employer
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
-
D.
professional
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
E.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
- 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_69a8871289048190b00b0d7744b7b2b1 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb3f9a87c8190816db3888787ad76 |
completed | March 7, 2026, 5:13 a.m. |
| PD | Predicate disambiguation | batch_69abaff9a09c8190a81fa13f4b85bc79 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:36 p.m.