Triple
T4792211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lidice massacre |
E106628
|
entity |
| Predicate | numberOfMenExecuted |
P867
|
FINISHED |
| Object | over 170 |
—
|
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 170 | Statement: [Lidice massacre, numberOfMenExecuted, over 170]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMenExecuted Context triple: [Lidice massacre, numberOfMenExecuted, over 170]
-
A.
numberOfPeopleExecuted
chosen
Indicates the total count of individuals who were put to death, typically as a result of a formal execution process.
-
B.
numberOfPeopleExecutedByHanging
Indicates the count of individuals who were put to death specifically by the method of hanging.
-
C.
notablePrisonerExecuted
Indicates that a person, recognized as a notable or significant prisoner, was executed while in custody.
-
D.
numberSentencedToDeath
Indicates the number of individuals who have been formally sentenced to receive the death penalty.
-
E.
notableOfficeHolderExecuted
Indicates that a person who held a notable or prominent office was executed.
- 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_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. |
Created at: March 20, 2026, 1:22 p.m.