Triple
T21688942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle |
E535305
|
entity |
| Predicate | hasFamousBearer |
P458
|
FINISHED |
| Object | Michelle Fairley |
—
|
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: Michelle Fairley | Statement: [Michelle, hasFamousBearer, Michelle Fairley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelle Fairley Context triple: [Michelle, hasFamousBearer, Michelle Fairley]
-
A.
Michelle Fairley
chosen
Michelle Fairley is a Northern Irish actress best known for playing Catelyn Stark in the television series "Game of Thrones."
-
B.
Lena Headey
Lena Headey is an English actress best known for playing Cersei Lannister in the television series "Game of Thrones."
-
C.
Helena Carter
Helena Carter was an American film actress known for her roles in 1940s and 1950s Hollywood productions, particularly in science fiction and adventure films.
-
D.
Morven Christie
Morven Christie is a Scottish actress known for her work in British television dramas, films, and theatre, including prominent roles in series such as "The A Word," "Grantchester," and "The Bay."
-
E.
Tessa Menzies
Tessa Menzies is a child of California politician and governor Gavin Newsom.
- 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_69e0c469b6ec8190aee4cadd1527db91 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef96cd51d481908df67e4f69826b06 |
completed | April 27, 2026, 5:03 p.m. |
Created at: April 16, 2026, 6:44 p.m.