Triple
T17625979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Pauline of Orange-Nassau |
E429843
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Pauline |
—
|
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: Pauline | Statement: [Princess Pauline of Orange-Nassau, givenName, Pauline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pauline Context triple: [Princess Pauline of Orange-Nassau, givenName, Pauline]
-
A.
Pauline
chosen
Pauline is a feminine given name used in various languages, often considered the female form of Paul.
-
B.
Paula
Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
-
C.
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.
-
D.
Livia Stone
Livia Stone is the wife of Biz Stone, the co-founder of Twitter and a prominent American entrepreneur.
-
E.
Bernardine
Bernardine is a 1957 musical comedy film starring Pat Boone in one of his early leading screen roles.
- 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_69d889e37f308190a6aa0a69daff86c7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46dbc62e88190b9757dc7c52d7fee |
completed | April 19, 2026, 5:53 a.m. |
Created at: April 10, 2026, 5:52 a.m.