Triple
T1141233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Nurse |
E23454
|
entity |
| Predicate | careerStatus |
P24370
|
FINISHED |
| Object | retired |
—
|
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: retired | Statement: [Richard Nurse, careerStatus, retired]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: careerStatus Context triple: [Richard Nurse, careerStatus, retired]
-
A.
hadOccupationStatusUntil
Indicates that an entity held a particular occupational status up to, but not necessarily beyond, a specified point in time.
-
B.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
C.
businessCareer
Indicates a relationship where an entity’s professional life, roles, or progression is specifically within the field of business or commerce.
-
D.
laterCareer
Indicates that the associated information or events pertain to a later stage or phase in an entity’s professional life or career trajectory.
-
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. 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc290ae08190afbf7e7ea2100d9e |
completed | March 1, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4d4104819084027a043c6118cb |
completed | March 1, 2026, 10:18 p.m. |
| PDg | Predicate description generation | batch_69a4bbb9fb4c81909dd39c496893c21b |
completed | March 1, 2026, 10:20 p.m. |
Created at: March 1, 2026, 7:44 p.m.