Triple
T17471334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica Olsson |
E425420
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jessica Olsson |
—
|
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: Jessica Olsson | Statement: [Jessica Olsson, name, Jessica Olsson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jessica Olsson Context triple: [Jessica Olsson, name, Jessica Olsson]
-
A.
Jessica Olsson
chosen
Jessica Olsson is a Swedish-Kenyan art gallery director best known as the wife of retired NBA star Dirk Nowitzki.
-
B.
Catherine Olsson
Catherine Olsson is a researcher and entrepreneur known for her work in AI safety and interpretability, including co-founding the AI company Anthropic.
-
C.
Beth Johanssen
Beth Johanssen is a brilliant young NASA systems operator and communications specialist who is part of the Ares 3 crew in Andy Weir’s science fiction novel "The Martian."
-
D.
Laura Holmgren
Laura Holmgren is the wife of American political scientist and author Francis Fukuyama.
-
E.
Christine Olsen
Christine Olsen is an Australian film producer best known for her work on the acclaimed drama "Rabbit-Proof Fence."
- 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_69d889dbc2e88190b18ea6115e819258 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451abab908190b6d9d8a64f7c2ea3 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:47 a.m.