Triple
T20321984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Free Syrian Army |
E492226
|
entity |
| Predicate | receivesSupportFrom |
P1704
|
FINISHED |
| Object | Saudi Arabia |
—
|
NE NERFINISHED |
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: Saudi Arabia | Statement: [Free Syrian Army, receivesSupportFrom, Saudi Arabia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saudi Arabia Context triple: [Free Syrian Army, receivesSupportFrom, Saudi Arabia]
-
A.
Saudi Arabia
chosen
Saudi Arabia is a Middle Eastern kingdom on the Arabian Peninsula known for its vast oil reserves, custodianship of Islam’s holiest sites, and significant geopolitical influence.
-
B.
KSA
KSA is the IATA airport code for Kosrae International Airport, which serves the island of Kosrae in the Federated States of Micronesia.
-
C.
Sulaymaniya
Sulaymaniya is a sub-school within the Zaydi branch of Shia Islam, distinguished by its own specific theological and legal interpretations.
-
D.
Arabistan
Arabistan is a region associated with Arab separatist aspirations, particularly within the context of movements seeking autonomy or independence from Iran.
-
E.
Qatar
Qatar is a wealthy Gulf nation on the Arabian Peninsula known for its vast natural gas reserves, rapid modernization, and large expatriate workforce.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a0134081909113563e1c3ba68a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6778c4430819083b17da5067b88ef |
completed | April 20, 2026, 6:59 p.m. |
Created at: April 16, 2026, 11:20 a.m.