Triple
T3628335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | murder of Jean-Paul Marat |
E76893
|
entity |
| Predicate | hasPerpetratorGender |
P49636
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [murder of Jean-Paul Marat, hasPerpetratorGender, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPerpetratorGender Context triple: [murder of Jean-Paul Marat, hasPerpetratorGender, female]
-
A.
hasPerformerGender
Indicates that an action, event, or performance is associated with the gender of the performer who carries it out.
-
B.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
C.
hasPerpetratorOccupation
Indicates that the occupation or job role of the perpetrator involved in an act or incident is being specified.
-
D.
perpetratedBy
Indicates that an action, event, or wrongdoing was carried out or caused by a particular agent or entity.
-
E.
perpetratorNationality
Indicates the country or nationality to which a perpetrator of an act, crime, or harmful event belongs.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2df2b708190afef6925a53ec551 |
completed | March 8, 2026, 6:41 p.m. |
| PD | Predicate disambiguation | batch_69adb8410a5881909c94818d7060b2b0 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb902e61c81908f10494f828e260f |
completed | March 8, 2026, 5:59 p.m. |
Created at: March 8, 2026, 3:23 p.m.