Triple
T17108066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syria–France relations |
E415150
|
entity |
| Predicate | FranceForeignPolicyGoal |
P15014
|
FINISHED |
| Object | support for a political transition in Syria |
—
|
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: support for a political transition in Syria | Statement: [Syria–France relations, FranceForeignPolicyGoal, support for a political transition in Syria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FranceForeignPolicyGoal Context triple: [Syria–France relations, FranceForeignPolicyGoal, support for a political transition in Syria]
-
A.
objectiveOfFrance
chosen
Indicates that something is an objective, goal, or aim pursued by France.
-
B.
FrenchObjective
Indicates that an entity serves as the goal, target, or object of an action or relation specifically within a French linguistic or contextual framework.
-
C.
primaryObjectiveOfFrench
Indicates that something is the main or foremost goal, aim, or purpose associated with France or French entities.
-
D.
effectOnFrance
Indicates the impact, influence, or consequences that something has on France.
-
E.
goalsForFrance
Indicates that the subject scored a goal while playing for the France national team.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc280b0c8190b9e620b90e0d4b40 |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.