Triple
T8521620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Room 101 |
E201704
|
entity |
| Predicate | effectOnPrisoners |
P83128
|
FINISHED |
| Object | forced confessions |
—
|
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: forced confessions | Statement: [Room 101, effectOnPrisoners, forced confessions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnPrisoners Context triple: [Room 101, effectOnPrisoners, forced confessions]
-
A.
hasPrisoners
Indicates that an entity holds or contains one or more individuals who are imprisoned or detained.
-
B.
usedForImprisoning
Indicates that something serves as a means, tool, or method for confining or detaining someone against their will.
-
C.
inmates
Indicates that one entity is confined or held as a prisoner within an institution or facility associated with another entity.
-
D.
hasNotableCategoryOfPrisoners
Indicates that a prison is known for housing a specific, notable category or type of prisoners.
-
E.
manyPrisonersCondition
Indicates a situation in which a large number of individuals are held in prison or detention, emphasizing the condition of having many prisoners.
- 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe62a490481908ee0ad4ba9a94682 |
completed | March 31, 2026, 3:20 p.m. |
| PD | Predicate disambiguation | batch_69cbd10f64b4819080859057c19e58f0 |
completed | March 31, 2026, 1:50 p.m. |
| PDg | Predicate description generation | batch_69cbe30d453481908f897ed2b06e7534 |
completed | March 31, 2026, 3:06 p.m. |
Created at: March 30, 2026, 6:16 p.m.