Triple
T348979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Office of Inspector General (U.S. Department of Transportation) |
E6999
|
entity |
| Predicate | typeOfInvestigation |
P11082
|
FINISHED |
| Object | criminal investigation |
—
|
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: criminal investigation | Statement: [Office of Inspector General (U.S. Department of Transportation), typeOfInvestigation, criminal investigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfInvestigation Context triple: [Office of Inspector General (U.S. Department of Transportation), typeOfInvestigation, criminal investigation]
-
A.
investigatedBy
Indicates that an entity is the subject of an investigation carried out by another entity.
-
B.
typeOfAudit
Indicates the specific category or kind of audit being performed or referenced in relation to an entity.
-
C.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
D.
screeningType
Indicates the specific method or category of screening applied in a screening process or evaluation.
-
E.
hasTypeOfExperiment
Indicates that an experiment is associated with or classified under a specific type or category of experiment.
- 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb1dc5f88190b54d084c6def7fc5 |
completed | Feb. 28, 2026, 1:18 p.m. |
| PD | Predicate disambiguation | batch_69a2e955d1f88190bd687c46fa7c5469 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea60e590819081779a6510918d9b |
completed | Feb. 28, 2026, 1:15 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.