Triple
T18562548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martha Hennessy |
E453680
|
entity |
| Predicate | hasBeenArrestedFor |
P15395
|
FINISHED |
| Object | acts of civil disobedience |
—
|
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: acts of civil disobedience | Statement: [Martha Hennessy, hasBeenArrestedFor, acts of civil disobedience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeenArrestedFor Context triple: [Martha Hennessy, hasBeenArrestedFor, acts of civil disobedience]
-
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.
hasReasonForArrest
Indicates that an arrest is associated with a specific reason or cause.
-
C.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
D.
numberOfArrests
Indicates the count of times an entity has been arrested.
-
E.
hasBeenImprisoned
Indicates that an entity has been confined or incarcerated in a prison or similar detention facility at some point in time.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53afb4f088190adf0b2b64057a210 |
completed | April 19, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69e478c16e0c8190b03966aa23c395a6 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:42 a.m.