Triple
T3901418
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwenninger Wild Wings |
E90497
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
SWW
SWW is the common abbreviation for the Schwenninger Wild Wings, a professional ice hockey team based in Schwenningen, Germany.
|
E398243
|
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: SWW | Statement: [Schwenninger Wild Wings, abbreviation, SWW]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SWW Context triple: [Schwenninger Wild Wings, abbreviation, SWW]
-
A.
SWC
SWC is the abbreviation for the Southwest Conference, a former NCAA Division I college athletic conference that primarily featured schools from Texas and the surrounding region.
-
B.
Wast Water
Wast Water is a deep, glacial lake in England’s Lake District, renowned for its dramatic surrounding peaks and remote, rugged scenery.
-
C.
UUWW
UUWW is the ICAO airport code assigned to Vnukovo International Airport in Moscow, Russia.
-
D.
SWP
SWP is the commonly used acronym for California’s State Water Project, a massive water storage and delivery system supplying water to millions of residents and vast agricultural areas.
-
E.
WW
WW is the commonly used abbreviation for Woodsworth College, a constituent college of the University of Toronto known for its diverse student body and focus on continuing and part-time education.
- 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: SWW Triple: [Schwenninger Wild Wings, abbreviation, SWW]
Generated description
SWW is the common abbreviation for the Schwenninger Wild Wings, a professional ice hockey team based in Schwenningen, Germany.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SWW Target entity description: SWW is the common abbreviation for the Schwenninger Wild Wings, a professional ice hockey team based in Schwenningen, Germany.
-
A.
SWC
SWC is the abbreviation for the Southwest Conference, a former NCAA Division I college athletic conference that primarily featured schools from Texas and the surrounding region.
-
B.
Wast Water
Wast Water is a deep, glacial lake in England’s Lake District, renowned for its dramatic surrounding peaks and remote, rugged scenery.
-
C.
UUWW
UUWW is the ICAO airport code assigned to Vnukovo International Airport in Moscow, Russia.
-
D.
SWP
SWP is the commonly used acronym for California’s State Water Project, a massive water storage and delivery system supplying water to millions of residents and vast agricultural areas.
-
E.
WW
WW is the commonly used abbreviation for Woodsworth College, a constituent college of the University of Toronto known for its diverse student body and focus on continuing and part-time education.
- 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_69aed95d315881908cbf1bf4a7215fbf |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecf2f230819099abc109a0b7d916 |
completed | March 9, 2026, 3:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51ca7636081908f98c4e22617f808 |
completed | March 14, 2026, 8:30 a.m. |
| NEDg | Description generation | batch_69b5207c0cfc8190aae16e8a88348679 |
completed | March 14, 2026, 8:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b52163bf888190b38f87d22ecd200e |
completed | March 14, 2026, 8:50 a.m. |
Created at: March 9, 2026, 3:21 p.m.