Triple
T31075963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | It Takes a Thief |
E791961
|
entity |
| Predicate | employingOrganizationInPlot |
P177511
|
FINISHED |
| Object | U.S. government |
—
|
NE NERFINISHED |
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: U.S. government | Statement: [It Takes a Thief, employingOrganizationInPlot, U.S. government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employingOrganizationInPlot Context triple: [It Takes a Thief, employingOrganizationInPlot, U.S. government]
-
A.
employerInPlot
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
B.
hasOrganizationInPlot
chosen
Indicates that an organization appears or plays a role within the narrative plot of a work.
-
C.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
D.
employerOrPublisherOf
Indicates that one entity serves as the employer or publishing organization responsible for another entity (such as a person or work).
-
E.
worksInOrganizationType
Indicates that an entity is employed by or performs work within an organization of a specified type (e.g., company, nonprofit, government agency).
- 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_69f224ccdbbc81909b0cdb4cc2d70c7a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f71996e1a48190ac59a1d66d7c44e8 |
completed | May 3, 2026, 9:47 a.m. |
| PD | Predicate disambiguation | batch_69f71820c6c88190ab38b4fa626d22cc |
completed | May 3, 2026, 9:40 a.m. |
Created at: April 29, 2026, 9:02 p.m.