Triple
T16833713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nat |
E409212
|
entity |
| Predicate | closeTo |
P350
|
FINISHED |
| Object | Dan |
E182921
|
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: Dan | Statement: [Nat, closeTo, Dan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Context triple: [Nat, closeTo, Dan]
-
A.
Dan
Dan is the protagonist of Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," a post-scarcity future resident of a reputation-based society centered around a Disney theme park.
-
B.
Dan
chosen
Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
-
C.
Dan
Dan is a central character in Louisa May Alcott's novel "Jo's Boys," known for his rough past, adventurous spirit, and deep loyalty to the Bhaer family.
-
D.
Dan
Dan is a character in the play "Clybourne Park," representing a contemporary figure who uncovers the neighborhood’s buried history and helps connect past events to present-day tensions.
-
E.
Dan
Dan, better known as the Duke of Zhou, was an influential early Zhou dynasty statesman and regent in ancient China renowned for consolidating royal power and shaping foundational political and ritual institutions.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b31981ac8190bbd9720efe842778 |
completed | April 18, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b2a4101081908faa1b85d338b05e |
completed | May 10, 2026, 4:30 p.m. |
Created at: April 10, 2026, 5:23 a.m.