Triple
T15046915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daisy Carter |
E379251
|
entity |
| Predicate | hasCriminalRecordFor |
P80418
|
FINISHED |
| Object | kidnapping |
—
|
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: kidnapping | Statement: [Daisy Carter, hasCriminalRecordFor, kidnapping]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCriminalRecordFor Context triple: [Daisy Carter, hasCriminalRecordFor, kidnapping]
-
A.
hasHadCriminalConviction
Indicates that an entity has previously been found guilty of a criminal offense through a legal process.
-
B.
criminalRecord
chosen
Indicates that an entity has a documented history of criminal offenses or convictions recorded by an authority.
-
C.
associatedWithCrimeRecordOfUser
Indicates a relationship where something is linked to, or derived from, the crime record belonging to a specific user.
-
D.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
E.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda8e64e48190873104a02a676ff3 |
completed | April 15, 2026, 12:23 a.m. |
| PD | Predicate disambiguation | batch_69de9a69d7848190b2b4662dd30f20e9 |
completed | April 14, 2026, 7:50 p.m. |
Created at: April 10, 2026, 3 a.m.