Triple
T20246225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pure Country |
E498430
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Jack Hofstra |
—
|
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: Jack Hofstra | Statement: [Pure Country, editedBy, Jack Hofstra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Hofstra Context triple: [Pure Country, editedBy, Jack Hofstra]
-
A.
Jack Hofstra
chosen
Jack Hofstra is a film editor known for his work on the Western action movie "Young Guns."
-
B.
David Hofstra
David Hofstra is an American bassist and composer best known for his work in the new wave and jazz scenes, including as a member of The Waitresses.
-
C.
Joe Flom
Joe Flom was a prominent American corporate lawyer and name partner at Skadden, Arps, Slate, Meagher & Flom, renowned for pioneering hostile takeover strategies in the late 20th century.
-
D.
Lewis J. Perelman
Lewis J. Perelman is an American author and policy analyst known for his work on education reform, technology, and economic competitiveness.
-
E.
Thomas Blatt
Thomas Blatt was a Polish Jewish Holocaust survivor, writer, and speaker best known for his escape from the Sobibor extermination camp and his later efforts to document its history.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673a26c8481908f408fb6426aece9 |
completed | April 20, 2026, 6:42 p.m. |
Created at: April 11, 2026, 11:40 p.m.