Triple
T24070418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | murder of Albert Snyder |
E596209
|
entity |
| Predicate | hasVictimSpouse |
P154746
|
FINISHED |
| Object | Ruth Snyder |
—
|
NE NERFINISHED |
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: Ruth Snyder | Statement: [murder of Albert Snyder, hasVictimSpouse, Ruth Snyder]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVictimSpouse Context triple: [murder of Albert Snyder, hasVictimSpouse, Ruth Snyder]
-
A.
spouseInvolvedIn
Indicates that a person's spouse participates in, is associated with, or plays a role in a specified activity, event, or situation.
-
B.
spouseExecuted
Indicates that one person’s spouse was put to death, typically as a result of a legal or extrajudicial execution.
-
C.
spouseExecutedFor
Indicates that one person’s spouse was put to death, typically as a result of a legal or punitive execution.
-
D.
spouseAccusation
Indicates that one spouse has accused the other spouse of some wrongdoing, misconduct, or blameworthy action.
-
E.
throughSpouse
Indicates a relationship or connection that exists indirectly between two entities by way of one entity’s spouse.
- 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_69e288c25c008190850cf447940ab181 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1db17c99881909f97e858fb183d86 |
completed | April 29, 2026, 10:19 a.m. |
| PD | Predicate disambiguation | batch_69f1764b1d4c8190b12590c6339c31c1 |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f1785afe3c81909be28986ffe944bf |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 10:41 p.m.