Triple
T12545074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monica Lewinsky |
E299940
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Monica Samille Lewinsky |
E299940
|
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: Monica Samille Lewinsky | Statement: [Monica Lewinsky, fullName, Monica Samille Lewinsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monica Samille Lewinsky Context triple: [Monica Lewinsky, fullName, Monica Samille Lewinsky]
-
A.
Monica Lewinsky
chosen
Monica Lewinsky is an American activist, writer, and former White House intern best known for her involvement in a 1990s political scandal with President Bill Clinton and her later work against cyberbullying and public shaming.
-
B.
Nicki Lewinsky
Nicki Lewinsky is one of Nicki Minaj’s provocative alter egos, often associated with her more sexually explicit and bold lyrical persona.
-
C.
Linda Tripp
Linda Tripp was a former U.S. civil servant whose secret recordings of Monica Lewinsky’s conversations with her played a central role in the impeachment of President Bill Clinton.
-
D.
Janet Hill
Janet Hill is known as the former wife of Apple co-founder Steve Wozniak.
-
E.
Mary Podesta
Mary Podesta is an American lawyer and privacy policy expert known for her work on data protection and technology issues, including senior roles in government and industry.
- 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_69d6ada707008190aaec1238117c9379 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9547f9a1c81908f54c58a116a8446 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f655801cac8190b1f9a72f8fed0399 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 9:57 p.m.