Triple
T4792213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lidice massacre |
E106628
|
entity |
| Predicate | numberOfChildrenKilled |
P58453
|
FINISHED |
| Object | over 80 |
—
|
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: over 80 | Statement: [Lidice massacre, numberOfChildrenKilled, over 80]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChildrenKilled Context triple: [Lidice massacre, numberOfChildrenKilled, over 80]
-
A.
killedApproximate
Indicates that one entity caused the death of another, but the information about this killing is uncertain, estimated, or not known with exact precision.
-
B.
killedAlongWith
Indicates that one entity was killed at the same time and in the same event or circumstance as another entity.
-
C.
killedDuring
Indicates that one entity caused the death of another entity in the course of, or as part of, a specified event or time period.
-
D.
numberOfChildren
Indicates the total count of children that an entity has.
-
E.
killedFamilyOf
Indicates that one entity caused the death of one or more members of another entity’s family.
- 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_69bd43f591c881909e5a532388b0f3f3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd66059bfc8190885d26d05dd38df1 |
completed | March 20, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69bd622e1b408190806c15c61519fc74 |
completed | March 20, 2026, 3:05 p.m. |
| PDg | Predicate description generation | batch_69bd631328fc81909b28ae0a2a3ed9bb |
completed | March 20, 2026, 3:09 p.m. |
Created at: March 20, 2026, 1:22 p.m.