Triple
T6270860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Disappearing Acts |
E140529
|
entity |
| Predicate | otherCharacterOccupation |
P34569
|
FINISHED |
| Object | construction worker |
—
|
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: construction worker | Statement: [Disappearing Acts, otherCharacterOccupation, construction worker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherCharacterOccupation Context triple: [Disappearing Acts, otherCharacterOccupation, construction worker]
-
A.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
B.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
C.
fictionalOccupation
chosen
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
D.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69c008cabc4081909723e2547c9d6cc0 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063bb340c8190ab81b249cefa91ca |
completed | March 22, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69c05606fb50819082d1a5a91e5030b6 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:25 p.m.