Triple
T20122918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary Glitter |
E490656
|
entity |
| Predicate | hasCriminalConviction |
P90191
|
FINISHED |
| Object | child sexual abuse |
—
|
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: child sexual abuse | Statement: [Gary Glitter, hasCriminalConviction, child sexual abuse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCriminalConviction Context triple: [Gary Glitter, hasCriminalConviction, child sexual abuse]
-
A.
hasHadCriminalConviction
chosen
Indicates that an entity has previously been found guilty of a criminal offense through a legal process.
-
B.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
-
C.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
D.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
-
E.
hasFormerInmate
Indicates that an entity previously housed or supervised an individual who was once an inmate there.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6673f5b4c8190bf9fb5f4e6b6a452 |
completed | April 20, 2026, 5:49 p.m. |
| PD | Predicate disambiguation | batch_69e54cfb0d0081908e789b9b57e96668 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:30 p.m.