Triple
T7830382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Back for Good |
E181350
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Chris Porter
Chris Porter is a music producer best known for his work on the hit song "Back for Good" by Take That.
|
E699231
|
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: Chris Porter | Statement: [Back for Good, producer, Chris Porter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chris Porter Context triple: [Back for Good, producer, Chris Porter]
-
A.
John Porter
John Porter is a British record producer and musician best known for his work on influential blues and rock albums.
-
B.
Scott Porter
Scott Porter is an American actor best known for his roles on television series such as "Friday Night Lights" and "Hart of Dixie."
-
C.
Ben Porterfield
Ben Porterfield is a technology entrepreneur best known as a co-founder of the business intelligence and data analytics company Looker.
-
D.
Sean Porter
Sean Porter is an American cinematographer known for his work on independent and genre films, including the thriller "Green Room."
-
E.
Darrell Porter
Darrell Porter was an American Major League Baseball catcher best known for his standout postseason performances in the late 1970s and early 1980s, including key roles with the Kansas City Royals and St. Louis Cardinals.
- 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: Chris Porter Triple: [Back for Good, producer, Chris Porter]
Generated description
Chris Porter is a music producer best known for his work on the hit song "Back for Good" by Take That.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chris Porter Target entity description: Chris Porter is a music producer best known for his work on the hit song "Back for Good" by Take That.
-
A.
John Porter
John Porter is a British record producer and musician best known for his work on influential blues and rock albums.
-
B.
Scott Porter
Scott Porter is an American actor best known for his roles on television series such as "Friday Night Lights" and "Hart of Dixie."
-
C.
Ben Porterfield
Ben Porterfield is a technology entrepreneur best known as a co-founder of the business intelligence and data analytics company Looker.
-
D.
Sean Porter
Sean Porter is an American cinematographer known for his work on independent and genre films, including the thriller "Green Room."
-
E.
Darrell Porter
Darrell Porter was an American Major League Baseball catcher best known for his standout postseason performances in the late 1970s and early 1980s, including key roles with the Kansas City Royals and St. Louis Cardinals.
- 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_69ca8282ccec819083c48efb72d21cf9 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb04ac013c81909533fa348776f50c |
completed | March 30, 2026, 11:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5a854fac8190802599615a0f7bc4 |
completed | March 31, 2026, 5:24 a.m. |
| NEDg | Description generation | batch_69cb762ce6208190a24438e26ae83785 |
completed | March 31, 2026, 7:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cbb615dc248190b57b4fc7c430517f |
completed | March 31, 2026, 11:55 a.m. |
Created at: March 30, 2026, 4:44 p.m.