Triple
T19546075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Fadden |
E489052
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tom Fadden |
—
|
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: Tom Fadden | Statement: [Tom Fadden, name, Tom Fadden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Fadden Context triple: [Tom Fadden, name, Tom Fadden]
-
A.
Tom Fadden
chosen
Tom Fadden was an American character actor known for his numerous supporting roles in classic films and early television from the 1930s through the 1960s.
-
B.
John Tusa
John Tusa is a British arts administrator, broadcaster, and former managing director of the BBC World Service and the Barbican Centre.
-
C.
Doug Coughlin
Doug Coughlin is a fictional, hard-drinking mentor bartender from the 1988 film "Cocktail," known for his cynical life maxims and influence on the protagonist.
-
D.
Jim Cavanaugh
Jim Cavanaugh is an American businessman and aviation enthusiast best known as the founder of the Cavanaugh Flight Museum, which preserves and displays historic aircraft.
-
E.
Darryl Philbin
Darryl Philbin is a laid-back yet sharp-witted warehouse foreman who becomes a key supporting character and later office employee in the U.S. version of The Office.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63876bacc8190b17e2087de679785 |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 10, 2026, 1:41 p.m.