Triple
T15089646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caderousse |
E360381
|
entity |
| Predicate | victimOfCrimeBy |
P51875
|
FINISHED |
| Object | his wife and her lover |
—
|
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: his wife and her lover | Statement: [Caderousse, victimOfCrimeBy, his wife and her lover]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: victimOfCrimeBy Context triple: [Caderousse, victimOfCrimeBy, his wife and her lover]
-
A.
coVictim
Indicates that two or more entities are victims in the same harmful event or incident.
-
B.
murderVictimOf
Indicates that one entity is the person who was killed by another entity in an act of murder.
-
C.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
D.
allegedVictimOf
Indicates that one entity is claimed or reported to have been harmed, wronged, or victimized by another entity, without asserting that the claim is proven.
-
E.
targetOfCrime
chosen
Indicates that the subject is the person, organization, or entity against whom the referenced crime is committed.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00277ea808190be3f002a8316eff1 |
completed | April 15, 2026, 9:26 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:04 a.m.