Triple
T31537373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donna W. Scott |
E804643
|
entity |
| Predicate | hasWorkHistoryIn |
P17879
|
FINISHED |
| Object | modeling |
—
|
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: modeling | Statement: [Donna W. Scott, hasWorkHistoryIn, modeling]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWorkHistoryIn Context triple: [Donna W. Scott, hasWorkHistoryIn, modeling]
-
A.
hasWorkedFor
Indicates that an entity has been employed by or has provided work or services to another entity.
-
B.
hasWorkedIn
chosen
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
-
C.
hasPastOccupation
Indicates that an entity previously held a particular job, role, or occupation in the past.
-
D.
hasMajorEmployerHistory
Indicates that an entity has a documented history of employment with a major or significant employer.
-
E.
hasHistoryIn
Indicates that an entity has a past involvement, presence, or record of activity within a particular domain, context, or location.
- 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fd76d1e5208190a6f26651492d1e3c |
completed | May 8, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69fd702a226c81908edfda00f4be4130 |
completed | May 8, 2026, 5:10 a.m. |
Created at: April 30, 2026, 10:04 p.m.