Triple
T34804645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Opti-Grab |
E1003319
|
entity |
| Predicate | legalOutcomeInPlot |
P30903
|
FINISHED |
| Object | company forced to pay large settlements |
—
|
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: company forced to pay large settlements | Statement: [Opti-Grab, legalOutcomeInPlot, company forced to pay large settlements]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalOutcomeInPlot Context triple: [Opti-Grab, legalOutcomeInPlot, company forced to pay large settlements]
-
A.
legalOutcome
Indicates the resulting legal status, decision, or consequence that follows from a legal process, action, or judgment.
-
B.
legalConclusion
Indicates that a situation, set of facts, or argument leads to or supports a specific determination or outcome under the law.
-
C.
outcomeOf
Indicates that one entity is the result, consequence, or product that arises from another entity, event, or process.
-
D.
legalCaseOutcomeAssociatedWith
chosen
Indicates that a particular legal case outcome is connected or linked to a specific related entity, such as a case, party, or legal proceeding.
-
E.
fictionalOutcome
Indicates that an event, action, or situation leads to a result that occurs only within a fictional, imagined, or non-real context.
- 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_69f76db600b88190989abdf08fce3b27 |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69ff79e7206c8190a809b5f2a6261378 |
completed | May 9, 2026, 6:16 p.m. |
| PD | Predicate disambiguation | batch_69ff798356b881908645074fb3a96517 |
completed | May 9, 2026, 6:14 p.m. |
Created at: May 3, 2026, 3:59 p.m.