Triple
T6388769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Fraser |
E143765
|
entity |
| Predicate | wasArrestedFor |
P15395
|
FINISHED |
| Object | opposition to conscription during World War I |
—
|
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: opposition to conscription during World War I | Statement: [Peter Fraser, wasArrestedFor, opposition to conscription during World War I]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasArrestedFor Context triple: [Peter Fraser, wasArrestedFor, opposition to conscription during World War I]
-
A.
arrestedFor
chosen
Indicates that an authority has taken someone into custody because they are suspected or accused of committing a specified offense or wrongdoing.
-
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.
arrests
Indicates that one entity, typically an authority figure, seizes and detains another entity under legal or official power.
-
D.
numberOfArrests
Indicates the count of times an entity has been arrested.
-
E.
attemptedArrestBy
Indicates that one entity tried, but may not have succeeded, to place another entity under arrest.
- 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_69c008dac1ec81909cef8157ccd69962 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0686b4f34819088ff07185b34e536 |
completed | March 22, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69c060eff524819094cee1c70a0c1ff4 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:34 p.m.