Triple
T20340717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dot Records |
E495731
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | Randy Wood |
—
|
NE NERFINISHED |
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: Randy Wood | Statement: [Dot Records, ownedBy, Randy Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Randy Wood Context triple: [Dot Records, ownedBy, Randy Wood]
-
A.
Randy Wood
chosen
Randy Wood was an American record executive best known as the founder of the influential mid-20th-century label Dot Records.
-
B.
Randy Waldrum
Randy Waldrum is an American soccer coach best known for his long tenure leading the Notre Dame women’s team to multiple NCAA titles and for managing both professional clubs and national women’s teams.
-
C.
Randy Woods
Randy Woods is a former American basketball player best known as a standout guard at La Salle University who went on to play in the NBA.
-
D.
Randy Kerber
Randy Kerber is an American pianist, composer, orchestrator, and studio musician known for his extensive work on film scores and popular recordings.
-
E.
Randy Oglesby
Randy Oglesby is an American character actor known for his numerous television and film roles, particularly in science fiction series such as various Star Trek franchises.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6783533a881909a12311bb9c66542 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:23 a.m.