Triple
T22937337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Long Kesh prison |
E569620
|
entity |
| Predicate | housedPrisonerType |
P102118
|
FINISHED |
| Object | Provisional IRA members |
—
|
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: Provisional IRA members | Statement: [Long Kesh prison, housedPrisonerType, Provisional IRA members]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: housedPrisonerType Context triple: [Long Kesh prison, housedPrisonerType, Provisional IRA members]
-
A.
prisonerType
chosen
Indicates the classification or category assigned to a prisoner within a correctional or detention system.
-
B.
prisonType
Indicates the specific category or classification of a prison associated with an entity.
-
C.
detentionType
Indicates the specific category or form of detention applied to an entity within a custodial or restrictive context.
-
D.
imprisonedWith
Indicates that two entities are confined or held in prison together at the same time and place.
-
E.
hasPrisoners
Indicates that an entity holds or contains one or more individuals who are imprisoned or detained.
- 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_69e24590862c8190858f180ad302adab |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18136bf448190afa04f8b55a8bb6e |
completed | April 29, 2026, 3:55 a.m. |
| PD | Predicate disambiguation | batch_69ef3b882e708190b0eb0c87021c75b8 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:45 p.m.