Triple
T8751967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeane Kirkpatrick |
E207980
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Jeane |
E217686
|
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: Jeane | Statement: [Jeane Kirkpatrick, hasGivenName, Jeane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeane Context triple: [Jeane Kirkpatrick, hasGivenName, Jeane]
-
A.
Jeane
chosen
Jeane is a feminine given name most notably associated with American diplomat and political scientist Jeane Kirkpatrick.
-
B.
Jeane Duane Jordan
Jeane Duane Jordan was an American political scientist and diplomat best known for serving as the first female U.S. Ambassador to the United Nations during the Reagan administration.
-
C.
Juanita
Juanita is a feminine given name of Spanish origin commonly used in English- and Spanish-speaking countries.
-
D.
Juanita
Juanita is a residential neighborhood in the city of Kirkland, Washington, known for its parks, waterfront access, and suburban community character.
-
E.
Laura Jeanne
Laura Jeanne is the birth name of American actress and producer Reese Witherspoon, known for films like "Legally Blonde" and "Walk the Line."
- 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_69ca835cd6b08190bd7c63db92f53c86 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5da8cc548190a31ad542d2faf2d5 |
completed | March 31, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf4326d8cc8190900f5f91da6ef6c8 |
completed | April 3, 2026, 4:33 a.m. |
Created at: March 30, 2026, 6:39 p.m.