Triple
T21242211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinking of ROKS Cheonan |
E523502
|
entity |
| Predicate | resultingTension |
P61198
|
FINISHED |
| Object | escalation of inter-Korean tensions |
—
|
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: escalation of inter-Korean tensions | Statement: [Sinking of ROKS Cheonan, resultingTension, escalation of inter-Korean tensions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resultingTension Context triple: [Sinking of ROKS Cheonan, resultingTension, escalation of inter-Korean tensions]
-
A.
tension
Indicates a state of strain, stress, or conflict existing between entities, often involving opposing forces, interests, or emotions.
-
B.
hasTension
chosen
Indicates the presence of strain, stress, or conflict between entities in their relationship or interaction.
-
C.
hasTypeOfTension
Indicates that one entity is associated with, or characterized by, a specific kind or category of tension.
-
D.
tensionArea
Indicates the region or extent over which mechanical or emotional tension is distributed or experienced.
-
E.
languageTension
Indicates a relationship where differing languages or language use create conflict, strain, or friction between entities.
- 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_69e0b513b89c81908b27147e91368db2 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7352453f88190b38de7b0108c683a |
completed | April 21, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69e5f61239708190ab7b3c83ae848a0d |
completed | April 20, 2026, 9:46 a.m. |
Created at: April 16, 2026, 3:47 p.m.