Triple
T22119222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Alan Price Set |
E546620
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | John Walters |
—
|
NE NERFINISHED |
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: John Walters | Statement: [The Alan Price Set, hasMember, John Walters]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Walters Context triple: [The Alan Price Set, hasMember, John Walters]
-
A.
John Walters
chosen
John Walters is a relatively common personal name shared by multiple individuals across various professions and public roles.
-
B.
Don Francis
Don Francis is an American epidemiologist and public health official known for his early work on HIV/AIDS research and prevention.
-
C.
Brian Matthews
Brian Matthews is an American actor best known for his role in the 1981 slasher film "The Burning."
-
D.
Lou Carducci
Lou Carducci is a fictional character played by American actress and model Mackenzie Foy.
-
E.
Tom Snyder
Tom Snyder was an American television personality and pioneering late-night talk show host known for his in-depth, conversational interview style on programs like "Tomorrow" and "The Late Late Show."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e38b3848190ac3a4fa97d56e65a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12950f5348190b204fbc347fd5dab |
completed | April 28, 2026, 9:40 p.m. |
Created at: April 16, 2026, 8:31 p.m.