Triple
T15386222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiefer Sutherland |
E367921
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object | Rachel Sutherland |
E445523
|
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: Rachel Sutherland | Statement: [Kiefer Sutherland, sibling, Rachel Sutherland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Sutherland Context triple: [Kiefer Sutherland, sibling, Rachel Sutherland]
-
A.
Rachel Sutherland
chosen
Rachel Sutherland is a Canadian television producer and production manager, known for her work on various TV series and as the daughter of actor Donald Sutherland.
-
B.
Sarah Sutherland
Sarah Sutherland is an American actress best known for her role as Catherine Meyer on the HBO political satire series "Veep."
-
C.
Sarah Sweeney
Sarah Sweeney is an actress known for her role in the historical drama television series "The Bastard Executioner."
-
D.
Rebecca Huntley
Rebecca Huntley is a film producer best known for her work on the animated feature "The Bad Guys."
-
E.
Tessa Sanger
Tessa Sanger is the passionate, musically gifted young heroine of Margaret Kennedy’s novel "The Constant Nymph," whose intense, unconventional love and emotional vulnerability drive much of the story’s drama.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e74ff70819094c1a85f51d6e228 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c78f36d88190a39f407c5d8dbc0d |
completed | May 10, 2026, 5:59 p.m. |
Created at: April 10, 2026, 3:19 a.m.