Triple
T7592274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basa Sasak |
E179764
|
entity |
| Predicate | ISO639-3Code |
P208
|
FINISHED |
| Object | sas |
E508397
|
NE 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: sas | Statement: [Basa Sasak, ISO639-3Code, sas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: sas Context triple: [Basa Sasak, ISO639-3Code, sas]
-
A.
sas
chosen
sas is the ISO 639-3 code for the Sasak language spoken primarily on the Indonesian island of Lombok.
-
B.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
C.
SAS
SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
-
D.
SAS
SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
-
E.
SAS
SAS is a major Scandinavian airline group that provides passenger and cargo air transport services primarily across Europe and to intercontinental destinations.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9b92c348190b547f0aacfb8d6be |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c86197fe0881908307a411cabdca7f |
completed | March 28, 2026, 11:17 p.m. |
Created at: March 27, 2026, 3:53 p.m.