Triple
T21269995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kolyma labor camps |
E524228
|
entity |
| Predicate | estimatedPrisonerDeaths |
P82361
|
FINISHED |
| Object | tens of thousands |
—
|
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: tens of thousands | Statement: [Kolyma labor camps, estimatedPrisonerDeaths, tens of thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedPrisonerDeaths Context triple: [Kolyma labor camps, estimatedPrisonerDeaths, tens of thousands]
-
A.
estimatedPrisonersKilled
chosen
Indicates the estimated number of prisoners who were killed in a given context or event.
-
B.
estimatedPrisoners
Indicates a relationship where a value represents the estimated number of prisoners associated with a particular entity or context.
-
C.
estimatedPrisonerCount
Indicates the estimated number of prisoners associated with a particular context, such as a location, time period, or event.
-
D.
numberSentencedToDeath
Indicates the number of individuals who have been formally sentenced to receive the death penalty.
-
E.
numberOfTortureVictims
Indicates the quantity of individuals who have been subjected to torture.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73651c9208190a87d45acd6fafaaa |
completed | April 21, 2026, 8:33 a.m. |
| PD | Predicate disambiguation | batch_69e5f6161dac8190b06009cd180e3ff7 |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 16, 2026, 4:01 p.m.