Triple
T8415397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blade Runner |
E198718
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Rachael |
E220850
|
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: Rachael | Statement: [Blade Runner, character, Rachael]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachael Context triple: [Blade Runner, character, Rachael]
-
A.
Rachael
chosen
Rachael is a feminine given name commonly used in English-speaking countries, often considered a variant of the biblical name Rachel.
-
B.
Rachael Blake
Rachael Blake is an Australian actress known for her acclaimed performances in film and television, including a prominent role in the psychological drama "Lantana."
-
C.
Marlena Rosenbluth
Marlena Rosenbluth is a glamorous circus performer and animal trainer who becomes the central love interest and emotional core of Sara Gruen’s novel "Water for Elephants."
-
D.
Caroline Rhea
Caroline Rhea is a Canadian actress and stand-up comedian best known for her television work, including her role as Aunt Hilda on the sitcom "Sabrina the Teenage Witch."
-
E.
Rachael Taylor
Rachael Taylor is an Australian actress known for her roles in films like "Transformers" and TV series such as "Jessica Jones."
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e443a08190983d9a0a61e0f781 |
completed | March 31, 2026, 8:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce0333a3488190ba30d03b1d7bacb1 |
completed | April 2, 2026, 5:48 a.m. |
Created at: March 30, 2026, 6:06 p.m.