Triple
T9839619
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Specialist |
E239187
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Jack Hofstra |
E821521
|
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: Jack Hofstra | Statement: [The Specialist, editedBy, Jack Hofstra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Hofstra Context triple: [The Specialist, 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.
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.
-
C.
David Frankfurter
David Frankfurter was a Jewish medical student best known for assassinating Swiss Nazi leader Wilhelm Gustloff in 1936, an event later revisited in Günter Grass’s novella "Crabwalk."
-
D.
Howard Gobioff
Howard Gobioff was a computer scientist and early Google engineer best known as a co-author of the influential Google File System paper that helped shape modern distributed storage systems.
-
E.
William Margulies
William Margulies was an American cinematographer known for his work on mid-20th-century Hollywood films and television productions.
- 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_69ca84e3f0c48190ada72a65ebd50efd |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb34b045481908f89abd576aab497 |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5d5484c8190a78ccd0e9816ba51 |
completed | April 5, 2026, 3:24 a.m. |
Created at: March 30, 2026, 8:33 p.m.