Triple
T34180619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edwin Torres |
E876802
|
entity |
| Predicate | legalExperienceInfluenced |
P199645
|
FINISHED |
| Object | crime novels |
—
|
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: crime novels | Statement: [Edwin Torres, legalExperienceInfluenced, crime novels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalExperienceInfluenced Context triple: [Edwin Torres, legalExperienceInfluenced, crime novels]
-
A.
legalDoctrineInfluenced
Indicates that one legal doctrine has shaped, informed, or contributed to the development or interpretation of another legal doctrine.
-
B.
hasCommonLegalInfluence
Indicates that two or more entities are subject to, shaped by, or governed under the same legal authority, framework, or precedent.
-
C.
influencedByCourtCase
Indicates that an entity’s state, decision, or development is shaped or altered as a result of a specific court case or its outcome.
-
D.
influencedCourtDecision
Indicates that one entity had an effect on or contributed to the outcome of a court’s decision regarding another entity or matter.
-
E.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
- F. None of above. chosen
Provenance (4 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_69f349ae640c8190b9cd220b5368d8b6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff49f888348190b9c55afa73b99e6a |
completed | May 9, 2026, 2:51 p.m. |
| PD | Predicate disambiguation | batch_69ff49614ef88190ac70b034c55ad738 |
completed | May 9, 2026, 2:49 p.m. |
| PDg | Predicate description generation | batch_69ff49f7db2c819094d488d13985334c |
completed | May 9, 2026, 2:51 p.m. |
Created at: May 1, 2026, 1:54 a.m.