Triple
T17025690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wheels on Meals |
E413057
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object |
Anders Nelsson
Anders Nelsson is a musician and composer best known for his work on film scores, particularly in Hong Kong cinema.
|
E1248826
|
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: Anders Nelsson | Statement: [Wheels on Meals, musicBy, Anders Nelsson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anders Nelsson Context triple: [Wheels on Meals, musicBy, Anders Nelsson]
-
A.
Torsten Söderberg
Torsten Söderberg was a Swedish businessman and philanthropist known for his significant contributions to research, culture, and society through the foundation bearing his name.
-
B.
Sture Lindgren
Sture Lindgren was the husband of renowned Swedish author Astrid Lindgren and worked as a businessman in Sweden.
-
C.
Göran Tunström
Göran Tunström was a Swedish novelist and poet known for his lyrical prose and explorations of memory, faith, and small-town life, particularly in works like "The Christmas Oratorio."
-
D.
Stig Nilsson
Stig Nilsson is a Norwegian violinist best known as a long-time concertmaster of the Oslo Philharmonic Orchestra.
-
E.
Göran Andersson
Göran Andersson is a Swedish academic and engineer known for his contributions to electric power systems and energy technology.
- 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: Anders Nelsson Triple: [Wheels on Meals, musicBy, Anders Nelsson]
Generated description
Anders Nelsson is a musician and composer best known for his work on film scores, particularly in Hong Kong cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anders Nelsson Target entity description: Anders Nelsson is a musician and composer best known for his work on film scores, particularly in Hong Kong cinema.
-
A.
Torsten Söderberg
Torsten Söderberg was a Swedish businessman and philanthropist known for his significant contributions to research, culture, and society through the foundation bearing his name.
-
B.
Sture Lindgren
Sture Lindgren was the husband of renowned Swedish author Astrid Lindgren and worked as a businessman in Sweden.
-
C.
Göran Tunström
Göran Tunström was a Swedish novelist and poet known for his lyrical prose and explorations of memory, faith, and small-town life, particularly in works like "The Christmas Oratorio."
-
D.
Stig Nilsson
Stig Nilsson is a Norwegian violinist best known as a long-time concertmaster of the Oslo Philharmonic Orchestra.
-
E.
Göran Andersson
Göran Andersson is a Swedish academic and engineer known for his contributions to electric power systems and energy technology.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a012ed2ad708190a250762997611569 |
completed | May 11, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_6a012f3285c481909b3de139bd7aee8b |
completed | May 11, 2026, 1:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a012fcf7dc08190af1851b56cf1667a |
completed | May 11, 2026, 1:24 a.m. |
Created at: April 10, 2026, 5:33 a.m.