Triple
T14628932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle Burke |
E343427
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Michelle Burke |
E343427
|
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: Michelle Burke | Statement: [Michelle Burke, name, Michelle Burke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michelle Burke Context triple: [Michelle Burke, name, Michelle Burke]
-
A.
Michelle Burke
chosen
Michelle Burke is an American actress best known for her roles in 1990s films such as "Dazed and Confused" and "Coneheads."
-
B.
Michelle Foster
Michelle Foster is a fictional character from the romantic comedy film "Chasing Liberty," which follows the adventures of the rebellious daughter of the U.S. President.
-
C.
Emily Burton
Emily Burton was the wife of English poet and playwright Gordon Bottomley, known primarily in relation to his personal life and correspondence.
-
D.
Mary Beth Lacey
Mary Beth Lacey is a dedicated, streetwise New York City police detective and working mother from the television series "Cagney & Lacey."
-
E.
Michelle Moran
Michelle Moran is the wife of American actor and producer Michael Chiklis.
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4a7c8fc81909d10c1f563d7d1e7 |
completed | April 14, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170d8c9f0819099a398814f49f0ed |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 1:26 a.m.