Triple
T20883868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Paula Olsen |
E514220
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Paula |
—
|
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: Paula | Statement: [Dr. Paula Olsen, hasGivenName, Paula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paula Context triple: [Dr. Paula Olsen, hasGivenName, Paula]
-
A.
Paula
chosen
Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
-
B.
Paula
Paula is the custom audio and I/O coprocessor used in Commodore Amiga computers, responsible for sound generation and handling certain input/output functions.
-
C.
Patricia
Patricia is a feminine given name of Latin origin, commonly used in English-speaking countries.
-
D.
Sandra
Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
-
E.
Sandra
Sandra is a fictional character portrayed by English actress Annabelle Wallis.
- 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_69e0b4f733f081908a401c0b7beb0b9f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c67bd32c819097301e330e358c49 |
completed | April 21, 2026, 12:36 a.m. |
Created at: April 16, 2026, 12:46 p.m.