Triple
T15120752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Sofie Bergen |
E361163
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Anna |
unclear NED1
|
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: Anna | Statement: [Anna Sofie Bergen, givenName, Anna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Context triple: [Anna Sofie Bergen, givenName, Anna]
-
A.
Anna
Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
-
B.
Anna
Anna is an actress known for portraying the ambitious and manipulative Lady Macbeth in a production of Shakespeare’s tragedy "Macbeth."
-
C.
Anna
Anna is a biblical figure in the Book of Tobit, known as Tobit's wife and the mother of Tobias.
-
D.
Anna
Anna is a woman whose full name is Mrs. Anna Smith.
-
E.
Anna
Anna of Moscow was a medieval Russian noblewoman and princess associated with the ruling dynasties of Muscovy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69d85a06450081909c5a14ea9851a15e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0059f69a881909929a037a0eef702 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec87ed2cc8190b4cae72774e0df7f |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 3:06 a.m.