Triple
T10816639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred Hoyle |
E255247
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Fred |
E807032
|
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: Fred | Statement: [Fred Hoyle, givenName, Fred]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fred Context triple: [Fred Hoyle, givenName, Fred]
-
A.
Fred
Fred is a laid-back, comic book–obsessed college student and enthusiastic member of the superhero team in Disney's animated film "Big Hero 6."
-
B.
Fred
Fred is a French luxury jewelry brand renowned for its elegant, contemporary designs and high-end craftsmanship, owned by the LVMH group.
-
C.
Fred
Fred is Ebenezer Scrooge’s cheerful and warm-hearted nephew in Charles Dickens’s novella "A Christmas Carol."
-
D.
Fred
Fred is a prolific Brazilian striker best known for his goal-scoring exploits with Fluminense and the Brazilian national team.
-
E.
Fred
chosen
Fred is the given name of Fred J. Koenekamp, an American cinematographer known for his work on films such as "Patton" and "The Towering Inferno."
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733eea03c8190a68f4d4f89f497a2 |
completed | April 9, 2026, 5:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb0f9e3a081908163b398d845deeb |
completed | April 14, 2026, 9:26 p.m. |
Created at: April 8, 2026, 9:18 p.m.