Triple
T8729387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bruno Walter |
E207214
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Walter |
E32053
|
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: Walter | Statement: [Bruno Walter, familyName, Walter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Walter Context triple: [Bruno Walter, familyName, Walter]
-
A.
Walter
chosen
Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
-
B.
Wilbert
Wilbert is the given first name of American character actor Bill Cobbs, known for his numerous supporting roles in film and television.
-
C.
Wally Fay
Wally Fay is a supporting character in the 1945 film noir "Mildred Pierce," known as a somewhat sleazy businessman entangled in the story’s web of betrayal and murder.
-
D.
Walter Nelson
Walter Nelson was an attorney who served on the defense team in the landmark Ossian Sweet murder trial, which challenged racial injustice in 1920s Detroit.
-
E.
Walter Forward
Walter Forward was a 19th-century American politician and lawyer who served as U.S. Secretary of the Treasury under President John Tyler.
- 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_69ca8358e4008190898471a59b96c301 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d19fdc88190860e0c9c93ab79ce |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf2923abc48190a5b6027c2e4f1db7 |
completed | April 3, 2026, 2:42 a.m. |
Created at: March 30, 2026, 6:37 p.m.