Triple
T19504851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crystal Burset |
E487993
|
entity |
| Predicate | hasFirstName |
P17
|
FINISHED |
| Object | Crystal |
—
|
NE NERFINISHED |
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: Crystal | Statement: [Crystal Burset, hasFirstName, Crystal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Crystal Context triple: [Crystal Burset, hasFirstName, Crystal]
-
A.
Crystal
Crystal is the surname of American actor, comedian, and filmmaker Billy Crystal, known for his work in film, television, and stand-up comedy.
-
B.
Crystal
Crystal is a central character in the 2013 neo-noir crime film "Only God Forgives," known for her cold, domineering presence and pivotal role in the story’s violent family dynamics.
-
C.
Crystal
chosen
Crystal is a feminine given name often associated with clarity and beauty, derived from the English word for clear, transparent mineral or glass.
-
D.
Crystal
Crystal is a statically typed, compiled programming language with Ruby-inspired syntax designed for high performance and concurrency.
-
E.
Crystal
Crystal is a suburban city in Hennepin County, Minnesota, located just northwest of Minneapolis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e635113fdc819098ea0f738d01925c |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.