Triple
T820050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Willy |
E17731
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Bill |
E17085
|
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: Bill | Statement: [Willy, relatedName, Bill]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Context triple: [Willy, relatedName, Bill]
-
A.
Bill
Bill is a film featuring Mickey Rooney in a critically acclaimed dramatic role portraying a man with an intellectual disability.
-
B.
Bill
chosen
Bill is a common masculine given name, typically used as a diminutive or nickname for William.
-
C.
Joe
Joe is the given name of Joe Nickell, an American investigator and author known for his work examining alleged paranormal and mysterious phenomena.
-
D.
Brian
Brian is a masculine given name of Irish origin that has become widely used in English-speaking countries.
-
E.
Jim
Jim is a common English given name, typically used as a diminutive or familiar form of James.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab6698d881908d8c5d91259f97ec |
completed | March 1, 2026, 9:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b84122cc81909b12b69e27d50008 |
completed | March 4, 2026, 4:42 a.m. |
Created at: March 1, 2026, 7:38 p.m.