Triple
T3623355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederick Rosier |
E76778
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Rosier
Rosier is a surname of French origin borne by various notable individuals across different fields.
|
E373775
|
NE FINISHED |
How this triple was built (4 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: Rosier | Statement: [Frederick Rosier, familyName, Rosier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosier Context triple: [Frederick Rosier, familyName, Rosier]
-
A.
Rosera
Rosera is a town in the Samastipur district of Bihar, India, known as a local commercial and administrative center.
-
B.
Rosa Gryphus
Rosa Gryphus is a central character in Alexandre Dumas' novel "The Black Tulip," known for her devotion, courage, and pivotal role in aiding the protagonist amid political intrigue.
-
C.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
-
D.
Rosa
Rosa is a celebrated poem by Nikki Giovanni that honors civil rights icon Rosa Parks and reflects on the broader struggle for racial justice.
-
E.
Rosa
Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rosier Triple: [Frederick Rosier, familyName, Rosier]
Generated description
Rosier is a surname of French origin borne by various notable individuals across different fields.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rosier Target entity description: Rosier is a surname of French origin borne by various notable individuals across different fields.
-
A.
Rosera
Rosera is a town in the Samastipur district of Bihar, India, known as a local commercial and administrative center.
-
B.
Rosa Gryphus
Rosa Gryphus is a central character in Alexandre Dumas' novel "The Black Tulip," known for her devotion, courage, and pivotal role in aiding the protagonist amid political intrigue.
-
C.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
-
D.
Rosa
Rosa is a celebrated poem by Nikki Giovanni that honors civil rights icon Rosa Parks and reflects on the broader struggle for racial justice.
-
E.
Rosa
Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
- F. None of above. chosen
Provenance (5 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_69ad85dae2fc81908d1ceadbc6af0089 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2bc79008190abe6900adcbda8de |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b43320955c8190910c0f15c80f41f4 |
completed | March 13, 2026, 3:54 p.m. |
| NEDg | Description generation | batch_69b43705642881909c62b7363a4f3a12 |
completed | March 13, 2026, 4:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4379cd04c81909246747bcc357261 |
completed | March 13, 2026, 4:13 p.m. |
Created at: March 8, 2026, 3:23 p.m.