Triple
T457968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Altmark |
E7273
|
entity |
| Predicate | numberOfPrisonersApproximate |
P13732
|
FINISHED |
| Object | around 300 British prisoners |
—
|
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: around 300 British prisoners | Statement: [Altmark, numberOfPrisonersApproximate, around 300 British prisoners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPrisonersApproximate Context triple: [Altmark, numberOfPrisonersApproximate, around 300 British prisoners]
-
A.
durationOfImprisonment
Indicates the length of time that an entity is or was held in imprisonment.
-
B.
placeOfDetention
Indicates the location or facility where an entity is or was held in detention.
-
C.
wasImprisonedIn
Indicates that an entity was held in confinement or incarcerated at a particular place or facility.
-
D.
manyPrisonersCondition
Indicates a situation in which a large number of individuals are held in prison or detention, emphasizing the condition of having many prisoners.
-
E.
imprisonedFor
Indicates that one entity is held in detention or jail as a consequence of, or in connection with, a specific reason, action, or offense committed by another entity or itself.
- 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efa3163081909acff040a22bd559 |
completed | Feb. 28, 2026, 1:37 p.m. |
| PD | Predicate disambiguation | batch_69a2ede614b88190be07425f5535f56d |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2ee8b56d08190bd625626353d01b4 |
completed | Feb. 28, 2026, 1:32 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.