Triple
T21064667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saudi Arabian National Guard |
E518935
|
entity |
| Predicate | acronym |
P43
|
FINISHED |
| Object | SANG |
—
|
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: SANG | Statement: [Saudi Arabian National Guard, acronym, SANG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SANG Context triple: [Saudi Arabian National Guard, acronym, SANG]
-
A.
SANG
chosen
SANG is the acronym for the Saudi Arabian National Guard, a key military and security force responsible for protecting the Saudi royal family, strategic facilities, and internal stability.
-
B.
Sang
Sang is a French term meaning "blood," often used to denote a deep, vivid red color.
-
C.
Sangan
Sangan is a town located in Pakistan’s Balochistan province within the Sibi District.
-
D.
Sung
Sung is the given name of actor Sung Kang, best known for his role as Han in the Fast & Furious film franchise.
-
E.
Songo
Songo is a small town in Mozambique known primarily for its proximity to the Cahora Bassa Dam and its role in supporting the dam’s operations and nearby communities.
- 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_69e0b505ef108190b25dd4033e2ff7eb |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6feb29e388190a80fa969a4daa606 |
completed | April 21, 2026, 4:36 a.m. |
Created at: April 16, 2026, 2:44 p.m.