Triple
T14745136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Russ |
E346447
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | William Russ |
E346447
|
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: William Russ | Statement: [William Russ, name, William Russ]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: William Russ Context triple: [William Russ, name, William Russ]
-
A.
William Russ
chosen
William Russ is an American actor best known for his work in film and television, including prominent roles in dramas and family sitcoms.
-
B.
Frank Moorhouse
Frank Moorhouse was an acclaimed Australian writer and essayist known for his innovative short-story cycles and the Edith trilogy, which explored Australian politics and diplomacy.
-
C.
F. H. Varley
F. H. Varley was a Canadian painter and founding member of the Group of Seven, renowned for his expressive landscapes and portraits.
-
D.
Henry Beam Piper
Henry Beam Piper was an American science fiction author best known for his mid-20th-century works such as the Terro-Human Future History stories and the novel "Little Fuzzy."
-
E.
Joseph MacDonald
Joseph MacDonald was an American cinematographer known for his work on numerous classic Hollywood films, particularly in the 1940s and 1950s.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d002708190a32a4a45e96fc389 |
completed | April 14, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb9638648190a2a3eb255ec5ae28 |
completed | May 8, 2026, 3:04 p.m. |
Created at: April 10, 2026, 1:30 a.m.