Triple
T13941859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hello, Larry |
E335274
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Ed. Weinberger |
E317634
|
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: Ed. Weinberger | Statement: [Hello, Larry, producer, Ed. Weinberger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ed. Weinberger Context triple: [Hello, Larry, producer, Ed. Weinberger]
-
A.
Ed. Weinberger
chosen
Ed. Weinberger is an American television producer and writer best known for his work on influential sitcoms such as The Mary Tyler Moore Show, Taxi, and The Cosby Show.
-
B.
Josef Weinberger
Josef Weinberger is a music publishing company known for issuing operettas and other theatrical works, particularly in the Central European tradition.
-
C.
Weinberger
Weinberger is a German-origin surname most notably associated with Caspar Weinberger, a former U.S. Secretary of Defense.
-
D.
Weinberger
Weinberger is a music publishing company known for issuing classical works, including compositions by major Romantic-era composers.
-
E.
Walter Neustadt Jr.
Walter Neustadt Jr. was a prominent American rancher, businessman, and philanthropist from Oklahoma, known for his leadership in agriculture and generous support of higher education.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2cf6e29881908ddb8efca9a456a3 |
completed | April 14, 2026, 12:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce8bbd8c819084703298ed6d9c87 |
completed | May 3, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:17 p.m.