Triple
T8299466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atlantic University Sport |
E194311
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
AUS
AUS is the governing body for university-level varsity sports in Atlantic Canada, organizing intercollegiate athletic competitions among its member institutions.
|
E724530
|
NE FINISHED |
How this triple was built (4 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 | Statement: [Atlantic University Sport, abbreviation, AUS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AUS Context triple: [Atlantic University Sport, abbreviation, AUS]
-
A.
AUS
AUS is the three-letter IATA airport code for Austin–Bergstrom International Airport, the primary commercial airport serving Austin, Texas.
-
B.
AU
AU is the commonly used abbreviation for the African Union, a continental organization that promotes political and economic cooperation among African states.
-
C.
AU
Aarhus University (AU) is a major public research university in Aarhus, Denmark, known for its broad range of academic programs and strong international profile.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: AUS Triple: [Atlantic University Sport, abbreviation, AUS]
Generated description
AUS is the governing body for university-level varsity sports in Atlantic Canada, organizing intercollegiate athletic competitions among its member institutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: AUS Target entity description: AUS is the governing body for university-level varsity sports in Atlantic Canada, organizing intercollegiate athletic competitions among its member institutions.
-
A.
AUS
AUS is the three-letter IATA airport code for Austin–Bergstrom International Airport, the primary commercial airport serving Austin, Texas.
-
B.
AU
AU is the commonly used abbreviation for the African Union, a continental organization that promotes political and economic cooperation among African states.
-
C.
AU
Aarhus University (AU) is a major public research university in Aarhus, Denmark, known for its broad range of academic programs and strong international profile.
-
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. chosen
Provenance (5 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_69ca82e50ebc81909aa7b260c76bd757 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7dfb60e48190bfa5de1c1496d3b5 |
completed | March 31, 2026, 7:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd68bcb43081909e3a8a00947d03f2 |
completed | April 1, 2026, 6:49 p.m. |
| NEDg | Description generation | batch_69cd6d574ba88190a2538897d201d8c4 |
completed | April 1, 2026, 7:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cd7e536724819095ad006bc0f013a4 |
completed | April 1, 2026, 8:21 p.m. |
Created at: March 30, 2026, 5:53 p.m.