Triple
T21043742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Goodman |
E518394
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Al Goodman |
—
|
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: Al Goodman | Statement: [Al Goodman, name, Al Goodman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Al Goodman Context triple: [Al Goodman, name, Al Goodman]
-
A.
Al Goodman
chosen
Al Goodman was an American conductor and bandleader best known for his work on Broadway and radio during the early to mid-20th century.
-
B.
Brian Goodman
Brian Goodman is an American actor and director known for his character roles in film and television, including a notable part in the crime drama series "Rizzoli & Isles."
-
C.
Roger Goodman
Roger Goodman is a television director and producer best known for directing major live broadcasts and award shows, including the Academy Awards.
-
D.
Bill Goodwin
Bill Goodwin was an American radio and television announcer and actor best known for his work on comedy programs in the 1940s and 1950s.
-
E.
Jeffrey Goodman
Jeffrey Goodman is an entrepreneur best known as a founder of the online auto insurance company Esurance.
- 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_69e0b50438e08190917e2538bb8bc034 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fcf1950081908ff9fe8719e1e81b |
completed | April 21, 2026, 4:28 a.m. |
Created at: April 16, 2026, 2:19 p.m.