Triple
T26089530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cookie Lyon |
E658077
|
entity |
| Predicate | hasCriminalHistory |
P80418
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Cookie Lyon, hasCriminalHistory, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCriminalHistory Context triple: [Cookie Lyon, hasCriminalHistory, yes]
-
A.
hasHadCriminalConviction
Indicates that an entity has previously been found guilty of a criminal offense through a legal process.
-
B.
hasCriminalCharacter
Indicates that an entity possesses traits, behaviors, or a reputation associated with criminal activity or unlawful conduct.
-
C.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
-
D.
hasCriminalElement
Indicates that the subject involves, contains, or is associated with an illegal or criminal component, activity, or characteristic.
-
E.
criminalRecord
chosen
Indicates that an entity has a documented history of criminal offenses or convictions recorded by an authority.
- 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_69ee5bbfc4d08190a1b206d0ac3a1e8d |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 26, 2026, 7:46 p.m.