Triple
T38431771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mona Lisa Vito |
E903821
|
entity |
| Predicate | givesTestimonyIn |
P4080
|
FINISHED |
| Object | courtroom scene |
—
|
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: courtroom scene | Statement: [Mona Lisa Vito, givesTestimonyIn, courtroom scene]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: givesTestimonyIn Context triple: [Mona Lisa Vito, givesTestimonyIn, courtroom scene]
-
A.
gaveTestimonyIn
chosen
Indicates that one entity provided formal testimony or a statement in an official proceeding, event, or context associated with another entity.
-
B.
hasTestimony
Indicates that an entity provides, contains, or is associated with a formal statement or account (testimony) about another entity or event.
-
C.
soughtTestimonyAgainst
Indicates that one party actively attempted to obtain another party’s testimony to be used against a specified target in a legal or investigative context.
-
D.
viewOnTestimony
Indicates a stance or evaluation someone holds regarding the credibility, role, or use of testimony as a source of knowledge or evidence.
-
E.
testimonyAffects
Indicates that one party’s testimony has an influence or impact on another entity, situation, or outcome.
- 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_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcd313e61c8190b174b331365b803f |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f6b2e08190bf0300ae7c9ae67a |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:31 p.m.