Triple
T22244811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIFA Trigrammes |
E549813
|
entity |
| Predicate | example |
P1259
|
FINISHED |
| Object | AUS for Australia |
—
|
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: AUS for Australia | Statement: [FIFA Trigrammes, example, AUS for Australia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AUS for Australia Context triple: [FIFA Trigrammes, example, AUS for Australia]
-
A.
AUS
AUS is the three-letter IATA airport code for Austin–Bergstrom International Airport, the primary commercial airport serving Austin, Texas.
-
B.
AUS
chosen
AUS is the three-letter country code for Australia, the host nation of the 2000 Summer Olympics in Sydney.
-
C.
AUS
AUS is the governing body for university-level varsity sports in Atlantic Canada, organizing intercollegiate athletic competitions among its member institutions.
-
D.
AU
AU is the commonly used abbreviation for Anna University, a prominent public technical university based in Chennai, India.
-
E.
AU
AU is a German vehicle registration code used on license plates to identify cars registered in the Erzgebirgskreis district of Saxony.
- 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_69e11e41d9408190bd770cf282e22753 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f132170e5081909b9dbb204abf2a45 |
completed | April 28, 2026, 10:17 p.m. |
Created at: April 16, 2026, 8:38 p.m.