Triple
T28904868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London, Airstrip One |
E733043
|
entity |
| Predicate | policingInFiction |
P115466
|
FINISHED |
| Object | Thought Police operations |
—
|
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: Thought Police operations | Statement: [London, Airstrip One, policingInFiction, Thought Police operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policingInFiction Context triple: [London, Airstrip One, policingInFiction, Thought Police operations]
-
A.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
B.
lawEnforcementInStory
chosen
Indicates that law enforcement personnel or activities are present, involved, or play a role within the narrative of the story.
-
C.
fictionalDetective
Indicates that the subject is a detective character who exists only in fiction rather than in real life.
-
D.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
E.
hasFictionalPoliceDepartment
Indicates that an entity is associated with or features a police department that exists only within a fictional or imaginary 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_69f05b096d208190958a57d2e4b5a93a |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f6afebd7ec8190ab696f363d84abf0 |
completed | May 3, 2026, 2:16 a.m. |
| PD | Predicate disambiguation | batch_69f6aca204148190850a3dc325bc07b7 |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 28, 2026, 8:06 a.m.