Triple
T16209600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TK Records |
E393421
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Peter Brown |
E1067578
|
NE FINISHED |
How this triple was built (2 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: Peter Brown | Statement: [TK Records, associatedWith, Peter Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Brown Context triple: [TK Records, associatedWith, Peter Brown]
-
A.
Peter Brown
Peter Brown was an American actor best known for his roles in 1950s–1960s television Westerns such as "Lawman" and "Laredo."
-
B.
Peter Brown
chosen
Peter Brown is an American songwriter best known for co-writing Madonna’s hit song "Material Girl."
-
C.
Peter Browne
Peter Browne is a relatively common personal name shared by multiple notable individuals across fields such as politics, religion, and academia.
-
D.
Peter Harvey
Peter Harvey is a set designer known for his work on the production "Diamonds."
-
E.
Edward Hall
Edward Hall was a 16th-century English lawyer, historian, and chronicler best known for his influential Tudor-era chronicle "The Union of the Two Noble and Illustre Families of Lancastre and Yorke."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e22711e4fc8190bf7a9f0c59b7889f |
completed | April 17, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0007916d3481909d475f1661d80e77 |
completed | May 10, 2026, 4:20 a.m. |
Created at: April 10, 2026, 5:03 a.m.