Triple
T1270428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reign of Terror |
E15695
|
entity |
| Predicate | estimatedNumberOfExecutions |
P19112
|
FINISHED |
| Object | 17000 |
—
|
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: 17000 | Statement: [Reign of Terror, estimatedNumberOfExecutions, 17000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedNumberOfExecutions Context triple: [Reign of Terror, estimatedNumberOfExecutions, 17000]
-
A.
numberOfExecutions
chosen
Indicates the count of times a particular action, process, or event has been carried out.
-
B.
estimatedNumberOfBlocks
Indicates the approximate count of discrete blocks associated with or involved in the given entity or context.
-
C.
executionModel
Indicates how a process, task, or operation is carried out or implemented, specifying the underlying method, strategy, or mechanism of its execution.
-
D.
repetitionCount
Indicates the number of times a particular event, action, or pattern is repeated within a given context.
-
E.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
- 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_69a4935a94308190bb92555b79032824 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4c0691d70819088e57c78ff34af1e |
completed | March 1, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69a4bede52a081909665d60acbe41d31 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.