Triple
T5031435
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosemary |
E113312
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Rose-Marie |
E356235
|
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: Rose-Marie | Statement: [Rosemary, hasVariant, Rose-Marie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose-Marie Context triple: [Rosemary, hasVariant, Rose-Marie]
-
A.
Rose-Marie
chosen
Rose-Marie is a popular 1924 operetta, with music by Rudolf Friml, known for its romantic plot set in the Canadian Rockies and songs like "Indian Love Call."
-
B.
Renée
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
-
C.
Estelle
Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
-
D.
Therese
Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
-
E.
Marie
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
- 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_69bd443775e48190a646ffbfc4334723 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73922a4c81908651c2d9b5e01cb6 |
completed | March 20, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c6df1b88190ad61c87a28312957 |
completed | March 21, 2026, 1:26 p.m. |
Created at: March 20, 2026, 1:36 p.m.