Triple
T12356270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Times |
E294620
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Deeley |
E226087
|
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: Deeley | Statement: [Old Times, hasCharacter, Deeley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deeley Context triple: [Old Times, hasCharacter, Deeley]
-
A.
Delaney
Delaney is a surname of Irish origin commonly borne by individuals and families in English-speaking countries.
-
B.
Debralee
Debralee is a feminine given name most notably associated with American actress Debralee Scott.
-
C.
Delly
Delly is the nickname of Australian professional basketball player Matthew Dellavedova, known for his gritty defense and tenure in the NBA, including a championship run with the Cleveland Cavaliers.
-
D.
Darley Dale
Darley Dale is a small town and civil parish in the Derbyshire Dales of England, known for its scenic setting near the Peak District and its historic railway heritage.
-
E.
Michael Deeley
chosen
Michael Deeley is a British film producer best known for his work on acclaimed films such as "The Deer Hunter" and "Blade Runner."
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f8e64dc81908c2242c68cd1b86e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62ab4cdec8190849604ef2ec498ba |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:54 p.m.