Triple
T18958835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Caddy |
E463854
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Howard Smith |
—
|
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: Howard Smith | Statement: [The Caddy, hasCastMember, Howard Smith]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Howard Smith Context triple: [The Caddy, hasCastMember, Howard Smith]
-
A.
Howard Smith
Howard Smith is a film editor best known for his work on major Hollywood productions, including the disaster film "Dante's Peak."
-
B.
Howard Smith
chosen
Howard Smith was an American character actor known for his supporting roles in mid-20th-century films and television, often portraying gruff authority figures.
-
C.
Howard Smith
Howard Smith was a British intelligence officer who worked at Bletchley Park during World War II, contributing to codebreaking efforts in Hut 3.
-
D.
Chris Haywood
Chris Haywood is an Australian actor known for his extensive work in film, television, and theatre since the 1970s.
-
E.
Chris Smith
Chris Smith is a long-serving Republican U.S. Representative from New Jersey known for his work on human rights and veterans’ issues.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d057a48190b82b5788b3281e28 |
completed | April 20, 2026, 7:29 a.m. |
Created at: April 10, 2026, noon