Triple
T2899412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burma Railway |
E62618
|
entity |
| Predicate | associatedWarCrimeType |
P7957
|
FINISHED |
| Object | forced labor |
—
|
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: forced labor | Statement: [Burma Railway, associatedWarCrimeType, forced labor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWarCrimeType Context triple: [Burma Railway, associatedWarCrimeType, forced labor]
-
A.
associatedWithCrimesAgainstHumanity
Indicates a relationship in which an entity is linked or connected to the planning, commission, support, or responsibility for crimes against humanity.
-
B.
warCrimesContext
Indicates a context in which actions or events are associated with the commission, investigation, or adjudication of war crimes.
-
C.
associatedWithWar
Indicates a relationship where an entity is connected or related to war, such as by involvement, influence, cause, or context.
-
D.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
E.
crimeType
chosen
Indicates the specific category or nature of the crime associated with an event or entity.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0ad7bbc8190822738baa6935b74 |
completed | March 7, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69abdd17bcdc8190aa47274a50ba4ad4 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:10 p.m.