Triple
T8470005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAS-2 |
E200255
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | SAS |
unclear NED1
|
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: [SAS-2, hasAbbreviation, SAS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAS Context triple: [SAS-2, hasAbbreviation, SAS]
-
A.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
B.
SAS
SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
-
C.
SAS
SAS is the common abbreviation for the San Antonio Silver Stars, a former Women’s National Basketball Association (WNBA) team based in San Antonio, Texas.
-
D.
SAS
SAS is the station code for San Antonio railway station.
-
E.
SAS
SAS is the common abbreviation for the San Antonio Scorpions, a former professional soccer team based in San Antonio, Texas.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69ca831a4f348190bfdd09250e86ae35 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe4d5dd088190a79050417f527f14 |
completed | March 31, 2026, 3:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce39f45c3081908ea50810f0a386d6 |
completed | April 2, 2026, 9:42 a.m. |
Created at: March 30, 2026, 6:11 p.m.