Triple
T13486533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Identity |
E318517
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | David Finfer |
E571147
|
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: David Finfer | Statement: [Identity, editedBy, David Finfer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Finfer Context triple: [Identity, editedBy, David Finfer]
-
A.
David Finfer
chosen
David Finfer was an American film editor known for his work on a wide range of Hollywood movies across several decades.
-
B.
Hal Finney
Hal Finney was a pioneering cryptographer and early Bitcoin developer who received the first Bitcoin transaction and made significant contributions to digital privacy and cryptocurrency.
-
C.
L. Peter Deutsch
L. Peter Deutsch is a computer scientist and software developer best known for creating the Ghostscript interpreter for the PostScript language and PDF files.
-
D.
Paul Vixie
Paul Vixie is an American computer scientist and Internet pioneer best known for his influential work on the Domain Name System (DNS), including major contributions to BIND and DNS infrastructure security.
-
E.
Steve Wiener
Steve Wiener is a cinema industry executive best known as the founder of Cineworld Group, one of the largest cinema chains in the United Kingdom.
- 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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf3a15b48190b63fb59e926a97ae |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f74638e2088190a126791f60b541c7 |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 9, 2026, 9:42 p.m.