Triple
T16697858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Wall |
E405762
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | George White |
E409265
|
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: George White | Statement: [High Wall, editedBy, George White]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: George White Context triple: [High Wall, editedBy, George White]
-
A.
George White
George White was a British Army general best known for commanding the garrison during the Siege of Ladysmith in the Second Boer War.
-
B.
George White
chosen
George White was a film editor active in mid-20th-century American cinema, known for his work on classic Hollywood productions.
-
C.
Kay Kyser
Kay Kyser was a popular American bandleader and radio personality of the 1930s and 1940s, best known for his “Kollege of Musical Knowledge” show and hit swing-era recordings.
-
D.
Tom Walls
Tom Walls is a film editor known for his work on the movie "Remember My Name."
-
E.
Tom Walls
Tom Walls was a British actor, director, and producer best known for his work in early 20th-century stage farces and films.
- 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_69d8838db21081909589220fd71440a4 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3832e93c48190a594c498e9cc901a |
completed | April 18, 2026, 1:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00919ee61c81909928dd26270e9614 |
completed | May 10, 2026, 2:09 p.m. |
Created at: April 10, 2026, 5:19 a.m.