Triple
T6597887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ležáky massacre |
E148520
|
entity |
| Predicate | numberOfExecutedAdults |
P71967
|
FINISHED |
| Object | 33 |
—
|
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: 33 | Statement: [Ležáky massacre, numberOfExecutedAdults, 33]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfExecutedAdults Context triple: [Ležáky massacre, numberOfExecutedAdults, 33]
-
A.
numberOfIndictedPersonsApproximate
Indicates an approximate count of persons who have been formally indicted in a given context or case.
-
B.
numberOfPerpetrators
Indicates the count of distinct individuals who carried out or participated in a particular act, event, or offense.
-
C.
numberOfPeopleAccused
Indicates the count of individuals who are formally alleged to have committed a particular act or offense.
-
D.
hasAdultVolunteers
Indicates that an entity is associated with one or more adult individuals who volunteer their time or services for it.
-
E.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acfd17388190bd0bb8b2371e7df1 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6cc988c0081909d22b86ca299331c |
completed | March 27, 2026, 6:29 p.m. |
Created at: March 27, 2026, 1:56 p.m.