Triple
T24917977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Men at Work |
E624040
|
entity |
| Predicate | hasOccupationForMainCharacters |
P21567
|
FINISHED |
| Object | garbage collectors |
—
|
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: garbage collectors | Statement: [Men at Work, hasOccupationForMainCharacters, garbage collectors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationForMainCharacters Context triple: [Men at Work, hasOccupationForMainCharacters, garbage collectors]
-
A.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
B.
hasCoProtagonistOccupation
Indicates that two or more co-protagonists share a specified occupation or professional role.
-
C.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
-
D.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
E.
hasMainCharacterFrom
Indicates that a work of fiction has a main character who originates from or belongs to a specified place, group, or source.
- 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_69e2fac889c081908e9ff686cb428e5a |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f44a417a58819081777e18dda149fd |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442b8479c8190a7c8e416ac9e28a0 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 5:28 a.m.