Triple
T6303806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyra Sedgwick |
E141321
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Sosie Bacon |
E365981
|
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: Sosie Bacon | Statement: [Kyra Sedgwick, hasChild, Sosie Bacon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sosie Bacon Context triple: [Kyra Sedgwick, hasChild, Sosie Bacon]
-
A.
Sosie Bacon
chosen
Sosie Bacon is an American actress known for roles in film and television, including the horror film "Smile" and the series "Mare of Easttown."
-
B.
Chris Bacon
Chris Bacon is an American film and television composer known for scoring projects such as the psychological horror series "Bates Motel."
-
C.
Thane Baker
Thane Baker was an American sprinter and Olympic medalist known for his achievements in the 100 m and 200 m events during the 1950s.
-
D.
Frank Partos
Frank Partos was a Hungarian-American screenwriter known for his work on notable Hollywood films in the mid-20th century, including influential psychological dramas.
-
E.
Dan Blocker
Dan Blocker was an American actor best known for his role as the gentle giant Hoss Cartwright on the classic television Western series "Bonanza."
- 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0645e150881908b49a07914dcbfd8 |
completed | March 22, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e43e085081908546fa120a43bf84 |
completed | March 27, 2026, 1:58 a.m. |
Created at: March 22, 2026, 4:27 p.m.