Triple
T27785609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HM Prison Reading |
E700950
|
entity |
| Predicate | prisonCategory |
P19110
|
FINISHED |
| Object | local prison |
—
|
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: local prison | Statement: [HM Prison Reading, prisonCategory, local prison]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: prisonCategory Context triple: [HM Prison Reading, prisonCategory, local prison]
-
A.
prisonType
chosen
Indicates the specific category or classification of a prison associated with an entity.
-
B.
inmates
Indicates that one entity is confined or held as a prisoner within an institution or facility associated with another entity.
-
C.
prisonerType
Indicates the classification or category assigned to a prisoner within a correctional or detention system.
-
D.
specialtyInPrison
Indicates that an entity’s area of professional focus or expertise specifically concerns prisons or the prison system.
-
E.
primaryPrisonerGroups
Indicates the main categories or groupings into which prisoners are primarily classified or organized.
- 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_69ef6a50d8088190acbf3dfbb06d8091 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f637d224c0819085f7af916d439d04 |
completed | May 2, 2026, 5:43 p.m. |
| PD | Predicate disambiguation | batch_69f6318ae6f08190b3f85f9201046a15 |
completed | May 2, 2026, 5:16 p.m. |
Created at: April 27, 2026, 5:24 p.m.