Triple
T7878635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan |
E182921
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Danny |
E567577
|
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: Danny | Statement: [Dan, hasVariant, Danny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danny Context triple: [Dan, hasVariant, Danny]
-
A.
Danny
Danny is the charismatic, hard-drinking World War I veteran whose inherited houses and loose community of friends drive the picaresque adventures in John Steinbeck’s novel "Tortilla Flat."
-
B.
Danny
Danny is the young, psychically gifted son of Jack Torrance in Stephen King’s horror novel "The Shining" and its film adaptations.
-
C.
Danny
Danny is the young boy protagonist of the science-fiction adventure film "Zathura: A Space Adventure," whose discovery of a mysterious board game launches the story’s intergalactic journey.
-
D.
Danny
chosen
Danny is a masculine given name, often used as a diminutive of Daniel.
-
E.
Danny
Danny is a supporting character in Woody Allen's 2013 drama film "Blue Jasmine," involved in the personal and emotional turmoil surrounding the protagonist's life.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39bd64e481909f699e7dd2818b8f |
completed | March 31, 2026, 3:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63a38e8081908c72d0b52efa1792 |
completed | April 1, 2026, 12:15 a.m. |
Created at: March 30, 2026, 4:57 p.m.