Triple
T8369052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanoi Hilton |
E197406
|
entity |
| Predicate | treatmentCharacterization |
P82344
|
FINISHED |
| Object | violations of the Geneva Conventions |
—
|
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: violations of the Geneva Conventions | Statement: [Hanoi Hilton, treatmentCharacterization, violations of the Geneva Conventions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatmentCharacterization Context triple: [Hanoi Hilton, treatmentCharacterization, violations of the Geneva Conventions]
-
A.
treatsCharacter
Indicates how one character behaves toward or interacts with another character, especially in terms of care, respect, or mistreatment.
-
B.
resultCharacterization
Indicates how the outcome of an event, process, or action is qualitatively described or characterized.
-
C.
treatment
Indicates that one entity is used as a medical or therapeutic intervention to address, manage, or cure a condition affecting another entity.
-
D.
subsequentCharacterization
Indicates that one characterization or description of something occurs later in time than, and in relation to, an earlier characterization of the same thing.
-
E.
trialCharacterization
Indicates the specific features, conditions, or parameters that define and distinguish a particular trial within an experimental or procedural context.
- F. None of above. chosen
Provenance (4 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_69ca82f56730819080cec5d991c76f4c |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb808f7c0481909fef5834cb6e7a3e |
completed | March 31, 2026, 8:06 a.m. |
| PD | Predicate disambiguation | batch_69cb70cd04b08190ab5f72afd22a7967 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb76d823b08190a54fadb50660cda5 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 6:01 p.m.