Triple
T27008201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Fifth Woman |
E680304
|
entity |
| Predicate | hasMotiveForCrimes |
P34163
|
FINISHED |
| Object | revenge |
—
|
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: revenge | Statement: [The Fifth Woman, hasMotiveForCrimes, revenge]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotiveForCrimes Context triple: [The Fifth Woman, hasMotiveForCrimes, revenge]
-
A.
hasMotiveOfCriminals
Indicates that the specified motive is attributed to or associated with the criminals in question.
-
B.
hasCriminalCharacter
Indicates that an entity possesses traits, behaviors, or a reputation associated with criminal activity or unlawful conduct.
-
C.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
D.
reasonForMurder
chosen
Indicates the motive or underlying cause that led someone to commit a murder.
-
E.
hasCriminalElement
Indicates that the subject involves, contains, or is associated with an illegal or criminal component, activity, or characteristic.
- 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_69eeeb53939c8190bd431f32b060f01f |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f71f8ee0688190bd025f27993452d3 |
completed | May 3, 2026, 10:12 a.m. |
| PD | Predicate disambiguation | batch_69f71cc405c08190863565609a4c8499 |
completed | May 3, 2026, 10 a.m. |
Created at: April 27, 2026, 7:02 a.m.