Triple
T21959963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Bueller |
E542298
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Katie Bueller |
—
|
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: Katie Bueller | Statement: [Tom Bueller, hasRelative, Katie Bueller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katie Bueller Context triple: [Tom Bueller, hasRelative, Katie Bueller]
-
A.
Katie Bueller
chosen
Katie Bueller is a character from the 1986 teen comedy film "Ferris Bueller's Day Off," known as Ferris Bueller's responsible and often exasperated older sister.
-
B.
Katie Carpenter
Katie Carpenter is a television producer best known for her executive production work on the medical dramedy series "This Is Going to Hurt."
-
C.
Katie Van Waldenberg
Katie Van Waldenberg is a fictional character from the comedy film "Blades of Glory," where she is involved in the rivalry-filled world of competitive figure skating.
-
D.
Katie Yeager
Katie Yeager is a reality television personality best known for appearing as one of the young mothers on MTV’s Teen Mom 3.
-
E.
Katie Featherston
Katie Featherston is an American actress best known for her role as Katie in the "Paranormal Activity" horror film series.
- 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_69e0c47fab1081908dc74a6545dbb051 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12454a290819094d4b56547816e3f |
completed | April 28, 2026, 9:19 p.m. |
Created at: April 16, 2026, 8 p.m.