Triple
T10474119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuol Sleng Genocide Museum |
E247001
|
entity |
| Predicate | numberOfSurvivorsKnown |
P11769
|
FINISHED |
| Object | fewer than 20 |
—
|
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: fewer than 20 | Statement: [Tuol Sleng Genocide Museum, numberOfSurvivorsKnown, fewer than 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSurvivorsKnown Context triple: [Tuol Sleng Genocide Museum, numberOfSurvivorsKnown, fewer than 20]
-
A.
numberOfChildrenSurvivors
Indicates the count of children who survived a particular event, condition, or situation.
-
B.
hasSurvivors
chosen
Indicates that one or more entities continue to exist or remain alive after a particular event, condition, or incident.
-
C.
numberOfSuspectedVictims
Indicates the count of individuals believed or alleged to be victims in a particular incident, case, or context.
-
D.
estimatedNumberOfPeopleSaved
Indicates the approximate count of individuals whose lives were preserved or harm was averted as a result of a particular action, intervention, or entity.
-
E.
numberOfVictimsKilled
Indicates the count of victims who were killed as a result of the referenced event or action.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5094e74048190a2c70ef32c50ba71 |
completed | April 7, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69d4fb84bafc8190819757b93620508a |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:21 p.m.