Triple
T14685702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Intercity V set |
E344901
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Blue Goose
Blue Goose is the popular nickname for a specific Intercity V set, a type of Australian intercity passenger train.
|
E1113903
|
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: Blue Goose | Statement: [Intercity V set, nickname, Blue Goose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blue Goose Context triple: [Intercity V set, nickname, Blue Goose]
-
A.
Bubblegoose
"Bubblegoose" is a track from rapper and producer The Carnival, known for its playful energy and distinctive, imaginative style.
-
B.
Gogo Dodo
Gogo Dodo is a zany, surreal cartoon character from the Tiny Toon Adventures series, known for his reality-bending antics and residence in the bizarre world of Wackyland.
-
C.
Blue Cow
Blue Cow is a popular ski area within the Perisher resort in New South Wales, Australia, known for its varied terrain and lift-accessed slopes.
-
D.
Tin Goose
Tin Goose is the popular nickname for the Ford Trimotor, an early all-metal American passenger and cargo aircraft widely used in the late 1920s and early 1930s.
-
E.
Gon Gon
"Gon Gon" is a popular Afrobeat song by Nigerian singer and producer Selebobo, known for its catchy rhythm and danceable vibe.
- 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: Blue Goose Triple: [Intercity V set, nickname, Blue Goose]
Generated description
Blue Goose is the popular nickname for a specific Intercity V set, a type of Australian intercity passenger train.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Blue Goose Target entity description: Blue Goose is the popular nickname for a specific Intercity V set, a type of Australian intercity passenger train.
-
A.
Bubblegoose
"Bubblegoose" is a track from rapper and producer The Carnival, known for its playful energy and distinctive, imaginative style.
-
B.
Gogo Dodo
Gogo Dodo is a zany, surreal cartoon character from the Tiny Toon Adventures series, known for his reality-bending antics and residence in the bizarre world of Wackyland.
-
C.
Blue Cow
Blue Cow is a popular ski area within the Perisher resort in New South Wales, Australia, known for its varied terrain and lift-accessed slopes.
-
D.
Tin Goose
Tin Goose is the popular nickname for the Ford Trimotor, an early all-metal American passenger and cargo aircraft widely used in the late 1920s and early 1930s.
-
E.
Gon Gon
"Gon Gon" is a popular Afrobeat song by Nigerian singer and producer Selebobo, known for its catchy rhythm and danceable vibe.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb56bdb8081909ff86440ba20fb1f |
completed | April 14, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde1876fdc81908a4fe3deebb7ff83 |
completed | May 8, 2026, 1:13 p.m. |
| NEDg | Description generation | batch_69fde6c7a8ac8190a80b6c6ed0b5b157 |
completed | May 8, 2026, 1:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fde73ca1dc8190b7fc13d7ffb6daf4 |
completed | May 8, 2026, 1:38 p.m. |
Created at: April 10, 2026, 1:28 a.m.