Triple
T13779940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Botched |
E331106
|
entity |
| Predicate | hasOccupationOfMainCast |
P110410
|
FINISHED |
| Object | plastic surgeon |
—
|
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: plastic surgeon | Statement: [Botched, hasOccupationOfMainCast, plastic surgeon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationOfMainCast Context triple: [Botched, hasOccupationOfMainCast, plastic surgeon]
-
A.
hasMainRole
Indicates that an entity holds the primary or most significant role in relation to another entity or context.
-
B.
hasMainPerformerOccupation
Indicates that an entity’s primary or main performer is associated with a specified occupation or professional role.
-
C.
hasNotableCrewMember
Indicates that an entity is associated with a crew member who is considered notable or distinguished in some way.
-
D.
hasCrewMember
Indicates that an entity includes or employs another entity as a member of its crew.
-
E.
leadActorOccupation
chosen
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02460a688190a27874f8d35819c7 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe97846c819093b00ea117b64e0d |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 10:11 p.m.