Triple
T4202472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rose and Marguerite |
E86099
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Rose and Marguerite |
E86099
|
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 and Marguerite | Statement: [Rose and Marguerite, hasName, Rose and Marguerite]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose and Marguerite Context triple: [Rose and Marguerite, hasName, Rose and Marguerite]
-
A.
Rose and Marguerite
chosen
Rose and Marguerite are the paired flowers that symbolize Saint Lucia’s cultural heritage and serve as its national floral emblem.
-
B.
Marguerite
Marguerite is a French given name, equivalent to Margaret, commonly used for women and also meaning "daisy" in French.
-
C.
Margot
Margot is a feminine given name of French origin, often associated with Margot Frank, the elder sister of diarist Anne Frank.
-
D.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
E.
Marguerite De La Motte
Marguerite De La Motte was an American silent film actress best known for her leading roles in early 1920s adventure and drama films.
- 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_69aed93b89f48190a31f6d57c760e42f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af037f1de4819088d6bf544317a694 |
completed | March 9, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5962296b8819084b91de3f48b7658 |
completed | March 14, 2026, 5:08 p.m. |
Created at: March 9, 2026, 3:49 p.m.