Triple
T4861434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Simon Jordan |
E108667
|
entity |
| Predicate | hasProfessionInStory |
P42552
|
FINISHED |
| Object | consulting doctor on criminal case |
—
|
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: consulting doctor on criminal case | Statement: [Dr. Simon Jordan, hasProfessionInStory, consulting doctor on criminal case]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionInStory Context triple: [Dr. Simon Jordan, hasProfessionInStory, consulting doctor on criminal case]
-
A.
hasNotableProfessionField
Indicates that an entity’s notable profession or occupation belongs to a particular professional field or domain.
-
B.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
C.
portraysProfession
Indicates that one entity depicts or represents another entity in a specific profession or occupational role.
-
D.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
E.
roleInStories
chosen
Indicates the specific function, position, or character part an entity plays within one or more stories.
- 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_69bd440b965081908b0557721cae6338 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d5f62b48190b367ed1b850cfbcb |
completed | March 20, 2026, 3:53 p.m. |
| PD | Predicate disambiguation | batch_69bd6c27334481909ba8ac80854f7d8e |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:26 p.m.