Triple
T13295533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gloria Hemingway |
E316669
|
entity |
| Predicate | arrestRecord |
P80418
|
FINISHED |
| Object | had multiple arrests related to public indecency and alcohol |
—
|
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: had multiple arrests related to public indecency and alcohol | Statement: [Gloria Hemingway, arrestRecord, had multiple arrests related to public indecency and alcohol]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: arrestRecord Context triple: [Gloria Hemingway, arrestRecord, had multiple arrests related to public indecency and alcohol]
-
A.
criminalRecord
chosen
Indicates that an entity has a documented history of criminal offenses or convictions recorded by an authority.
-
B.
arrestedFor
Indicates that an authority has taken someone into custody because they are suspected or accused of committing a specified offense or wrongdoing.
-
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.
arrestedAt
Indicates that an entity was apprehended or taken into custody at a specific location or during a specific event or 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6893708190aeebf4c47386cff7 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:28 p.m.