Triple
T9821979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cecil Collins |
E238553
|
entity |
| Predicate | hasHadCriminalConviction |
P90191
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Cecil Collins, hasHadCriminalConviction, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHadCriminalConviction Context triple: [Cecil Collins, hasHadCriminalConviction, true]
-
A.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
-
B.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
C.
hasFormerInmate
Indicates that an entity previously housed or supervised an individual who was once an inmate there.
-
D.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
-
E.
hasPrison
Indicates that one entity possesses, contains, or is the location of a prison associated with another entity.
- 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3147ecc81908cfca84c05a367d9 |
completed | April 2, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69cd03e01ea881909a7d93fc3994ace5 |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06abc9248190a506b64e9c516d03 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:31 p.m.