Triple
T9848489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salt Lake Stallions |
E239403
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | AAF |
E401186
|
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: AAF | Statement: [Salt Lake Stallions, abbreviation, AAF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AAF Context triple: [Salt Lake Stallions, abbreviation, AAF]
-
A.
AAF
chosen
AAF is the common abbreviation for the Alliance of American Football, a short-lived professional American football league that operated in 2019.
-
B.
AFF
AFF is the ASEAN Football Federation, the regional governing body for football in Southeast Asia under the Asian Football Confederation.
-
C.
AF
AF is the two-letter ISO 3166-1 alpha-2 country code assigned to Afghanistan for international standardization and referencing.
-
D.
AF
AF is a 16-bit register pair in the Zilog Z80 CPU that combines the accumulator (A) and the flags register (F) for arithmetic and logic operations.
-
E.
AF
AF is the two-letter IATA airline designator assigned to Air France, the flag carrier of France.
- 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb371894c8190971ba497a2801521 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5e6279c8190adb7e79ac1161bfa |
completed | April 5, 2026, 3:24 a.m. |
Created at: March 30, 2026, 8:34 p.m.