Triple
T7336852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Investigations Unit |
E169147
|
entity |
| Predicate | legalStatusOfActivities |
P2250
|
FINISHED |
| Object | illegal |
—
|
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: illegal | Statement: [Special Investigations Unit, legalStatusOfActivities, illegal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStatusOfActivities Context triple: [Special Investigations Unit, legalStatusOfActivities, illegal]
-
A.
legalAct
Indicates that an entity performs, enacts, or is involved in a formal legal action, measure, or proceeding under a legal framework.
-
B.
legalStatusInManyCountries
Indicates that the subject has a particular legal classification or standing that is recognized across numerous countries.
-
C.
usedLegalStatus
Indicates that one entity applies or relies on the legal status or classification of another entity in a given context.
-
D.
legalStatusOfProstitution
Indicates the legal status or regulatory condition under which prostitution is permitted, restricted, or prohibited in a given jurisdiction.
-
E.
hasLegalStatus
chosen
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
- 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_69c68a568a6481908f11e20db7bc8446 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69c6f028fd748190b2ea5c3081958a42 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:04 p.m.