Triple
T6945262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johanna Maria Ruyter |
E160781
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Johanna |
E103810
|
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: Johanna | Statement: [Johanna Maria Ruyter, givenName, Johanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johanna Context triple: [Johanna Maria Ruyter, givenName, Johanna]
-
A.
Johanna
chosen
Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
-
B.
Johanna
"Johanna" is a recurring, lyrically poignant love song from Stephen Sondheim's musical *Sweeney Todd: The Demon Barber of Fleet Street*.
-
C.
Joanna
Joanna is a woman mentioned in the New Testament as one of Jesus’ followers who witnessed his resurrection.
-
D.
Joanna
Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
-
E.
Julianna
Julianna is a feminine given name most notably borne by American actress Julianna Margulies.
- 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_69c68850419081909fb426b8f5a304c7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6da8a65c48190b6862fc60f6c7f7a |
completed | March 27, 2026, 7:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7586b6f0c8190a6caad7d020e9c4d |
completed | March 28, 2026, 4:26 a.m. |
Created at: March 27, 2026, 2:28 p.m.