Triple
T23987591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israel and Egypt |
E604979
|
entity |
| Predicate | relationsCharacterization |
P89493
|
FINISHED |
| Object | cold peace |
—
|
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: cold peace | Statement: [Israel and Egypt, relationsCharacterization, cold peace]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationsCharacterization Context triple: [Israel and Egypt, relationsCharacterization, cold peace]
-
A.
relationshipCharacterizedAs
chosen
Indicates that one relationship is described, defined, or typified in terms of another specified characteristic or relational type.
-
B.
relatedCharacterType
Indicates that one character has a specified type of relationship or role in connection to another character.
-
C.
relationshipToCharacter
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
-
D.
relatedCharacterContext
Indicates a contextual relationship between characters, such as roles, interactions, or situational connections that link them within a specific narrative or setting.
-
E.
trackRelationship
Indicates a connection in which one entity monitors, follows, or keeps a record of another entity’s state, behavior, or changes over time.
- 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_69e295463f7c8190b1c19dbd114641b9 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d38838f481909a52fccd392a92df |
completed | April 29, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69f1615994c48190a5de95d3f7e5cd0a |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:36 p.m.