Triple
T1892760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AG |
E41907
|
entity |
| Predicate | officeItAbbreviatesOversees |
P33117
|
FINISHED |
| Object | federal law enforcement in the United States |
—
|
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: federal law enforcement in the United States | Statement: [AG, officeItAbbreviatesOversees, federal law enforcement in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeItAbbreviatesOversees Context triple: [AG, officeItAbbreviatesOversees, federal law enforcement in the United States]
-
A.
includedOffice
Indicates that one office is contained within, or forms part of, another office or organizational unit.
-
B.
officeIsIn
Indicates that one office is located within or inside another specified place or building.
-
C.
controlledOffice
Indicates that one entity has authority over, manages, or directs the operations of a particular office or administrative location.
-
D.
office
Indicates that an entity holds or occupies an official position, role, or post within an organization or institution.
-
E.
otherOffice
Indicates that one office is an alternative or additional office associated with the same organization, person, or entity as another office.
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1480a6c81909fcf5cce4c42fed4 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe61bc48190ac9ead027df930e1 |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb11bfd2c8190a805372589f73238 |
completed | March 7, 2026, 5:01 a.m. |
Created at: March 4, 2026, 7:34 p.m.