Triple
T21688936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle |
E535305
|
entity |
| Predicate | hasFamousBearer |
P458
|
FINISHED |
| Object | Michelle Monaghan |
—
|
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 Monaghan | Statement: [Michelle, hasFamousBearer, Michelle Monaghan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelle Monaghan Context triple: [Michelle, hasFamousBearer, Michelle Monaghan]
-
A.
Michelle Monaghan
chosen
Michelle Monaghan is an American actress known for her roles in films such as "Gone Baby Gone" and the "Mission: Impossible" series, as well as various television dramas.
-
B.
Catherine McDermott
Catherine McDermott is a British design curator, writer, and academic known for her work in contemporary design and museum studies.
-
C.
Kelly Reilly
Kelly Reilly is an English actress known for her roles in films like "Flight" and the "Sherlock Holmes" series, as well as the television drama "Yellowstone."
-
D.
Eve Hewson
Eve Hewson is an Irish actress known for her roles in films like "The Knick," "Bridge of Spies," and the series "Bad Sisters."
-
E.
Lou Doillon
Lou Doillon is a French singer-songwriter, actress, and model known for her distinctive husky voice and work in both music and film.
- 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.