Triple
T14274807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon May |
E353887
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Trainer theme tune
The "Trainer" theme tune is a British television theme music composition created by Simon May for the BBC drama series "Trainer."
|
E1090504
|
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: Trainer theme tune | Statement: [Simon May, notableWork, Trainer theme tune]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trainer theme tune Context triple: [Simon May, notableWork, Trainer theme tune]
-
A.
Trayning
Trayning is a small rural town in Western Australia's Wheatbelt region, known for its grain farming and agricultural services.
-
B.
Trainor
Trainor is the surname of American pop singer-songwriter Meghan Trainor, known for hits like "All About That Bass."
-
C.
Trainsong
Trainsong is a semi-autobiographical novel by Jan Kerouac that continues her exploration of a turbulent, unconventional life shaped by her famous Beat Generation lineage.
-
D.
In Tune
In Tune is a long-running BBC Radio 3 magazine programme featuring live classical music performances, interviews with musicians, and arts news.
-
E.
Money Train
Money Train is a 1995 American action-comedy film about two foster brothers who work as New York City transit cops and become entangled in a plot to rob the subway system’s armored “money train.”
- 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: Trainer theme tune Triple: [Simon May, notableWork, Trainer theme tune]
Generated description
The "Trainer" theme tune is a British television theme music composition created by Simon May for the BBC drama series "Trainer."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Trainer theme tune Target entity description: The "Trainer" theme tune is a British television theme music composition created by Simon May for the BBC drama series "Trainer."
-
A.
Trayning
Trayning is a small rural town in Western Australia's Wheatbelt region, known for its grain farming and agricultural services.
-
B.
Trainor
Trainor is the surname of American pop singer-songwriter Meghan Trainor, known for hits like "All About That Bass."
-
C.
Trainsong
Trainsong is a semi-autobiographical novel by Jan Kerouac that continues her exploration of a turbulent, unconventional life shaped by her famous Beat Generation lineage.
-
D.
In Tune
In Tune is a long-running BBC Radio 3 magazine programme featuring live classical music performances, interviews with musicians, and arts news.
-
E.
Money Train
Money Train is a 1995 American action-comedy film about two foster brothers who work as New York City transit cops and become entangled in a plot to rob the subway system’s armored “money train.”
- 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_69d8278d25148190abf1a8c8f5f533ad |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6582f5308190969f4cfd724d9139 |
completed | April 14, 2026, 4:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd326d35808190bbf3f6bbc50554f4 |
completed | May 8, 2026, 12:46 a.m. |
| NEDg | Description generation | batch_69fd372c49d88190ad76477d24e48d59 |
completed | May 8, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd379feff081908a74d12782bedbee |
completed | May 8, 2026, 1:08 a.m. |
Created at: April 10, 2026, 1:10 a.m.