Triple
T4072496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sykes–Picot Agreement |
E86678
|
entity |
| Predicate | regionAllocatedToFrance |
P11466
|
FINISHED |
| Object | coastal 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: coastal Syria | Statement: [Sykes–Picot Agreement, regionAllocatedToFrance, coastal Syria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionAllocatedToFrance Context triple: [Sykes–Picot Agreement, regionAllocatedToFrance, coastal Syria]
-
A.
locatedInMetropolitanFrance
Indicates that the subject is geographically situated within the territory of metropolitan (continental) France.
-
B.
hasFrenchSector
chosen
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
C.
usesPrimaryFrenchGateway
Indicates that an entity routes its primary communications or connections through a main gateway located in or associated with French infrastructure or networks.
-
D.
statusInFrance
Indicates the legal, social, or official standing or condition that an entity has within the jurisdiction of France.
-
E.
capsForFrance
Indicates that an individual has made official playing appearances (caps) 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_69aed93ebe448190a1f1686e28740ac9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc22be988190a2b6575d4f5e0f7b |
completed | March 9, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69aef9061d2481908307cafc9e9b32c0 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:38 p.m.