Triple
T11518011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ann Telnaes |
E273081
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ann Telnaes |
E273081
|
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: Ann Telnaes | Statement: [Ann Telnaes, name, Ann Telnaes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Telnaes Context triple: [Ann Telnaes, name, Ann Telnaes]
-
A.
Ann Telnaes
chosen
Ann Telnaes is an American editorial cartoonist renowned for her incisive political commentary and distinctive visual style, recognized with top honors in her field.
-
B.
Anna Nolin
Anna Nolin is an American educator and school district leader who serves as superintendent of the Newton Public Schools in Massachusetts.
-
C.
Suzanne Verdal
Suzanne Verdal is a Canadian woman best known as the real-life muse who inspired Leonard Cohen’s song “Suzanne.”
-
D.
Annette Stroyberg
Annette Stroyberg was a Danish actress and model best known for her roles in European films of the late 1950s and 1960s.
-
E.
Maureen Swanson
Maureen Swanson was a British actress active in the 1950s, known for her roles in comedy and drama films before later becoming the Countess of Dudley.
- 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fcf927081908ef89eff7ad833b0 |
completed | April 10, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f018b2f394819084c3cfd589098c30 |
completed | April 28, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:36 p.m.