Triple
T18084429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crow |
E432796
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Dan Crow |
—
|
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: Dan Crow | Statement: [Crow, hasNotableBearer, Dan Crow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Crow Context triple: [Crow, hasNotableBearer, Dan Crow]
-
A.
Dan Crow
chosen
Dan Crow is an American children's musician and songwriter best known for performing the theme song to the Disney Channel series "Welcome to Pooh Corner."
-
B.
Jeff Schaffer
Jeff Schaffer is an American television writer, director, and producer best known for his work on comedy series such as Curb Your Enthusiasm and The League.
-
C.
Dan Harrow
Dan Harrow is the earnest, idealistic young farmer who serves as the central romantic lead in the stage musical and film "The Farmer Takes a Wife."
-
D.
Michael Krieger
Michael Krieger is a fictional character appearing in the story of "Watch Over Me."
-
E.
Michael Brun
Michael Brun is a Haitian DJ, record producer, and musician known for blending electronic dance music with Caribbean and Afro-Caribbean influences.
- 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_69d8b907d05c819083cc3bd6021089e6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4d9fcdbec819085752e7605ae7772 |
completed | April 19, 2026, 1:34 p.m. |
Created at: April 10, 2026, 10:27 a.m.