Triple
T29025523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Rawls |
E737580
|
entity |
| Predicate | deprioritizes |
P165934
|
FINISHED |
| Object | genuine police work |
—
|
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: genuine police work | Statement: [William Rawls, deprioritizes, genuine police work]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deprioritizes Context triple: [William Rawls, deprioritizes, genuine police work]
-
A.
prioritises
Indicates that one entity gives greater importance, preference, or precedence to another entity or task over alternatives.
-
B.
deprioritizedTheater
Indicates that a theater has been assigned lower priority relative to other options or tasks, reducing its importance or precedence in decision-making or scheduling.
-
C.
demotes
Indicates that one entity reduces another entity’s rank, status, or level within a hierarchy or system.
-
D.
planningPriority
Indicates the relative importance or urgency assigned to an item, task, or objective when organizing or scheduling plans.
-
E.
decline
Indicates a decrease or reduction in the level, amount, quality, or intensity of something over time.
- F. None of above. chosen
Provenance (4 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_69f077ef00fc81909325f084ad37c035 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f66008ba1c8190b629678511f36c99 |
completed | May 2, 2026, 8:35 p.m. |
| PD | Predicate disambiguation | batch_69f659d297cc8190b2b962ba30a1edb3 |
completed | May 2, 2026, 8:08 p.m. |
| PDg | Predicate description generation | batch_69f65ad638ac8190a17bb987fce53279 |
completed | May 2, 2026, 8:13 p.m. |
Created at: April 28, 2026, 9:52 a.m.