Triple
T13826405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heidelberg |
E332259
|
entity |
| Predicate | nearbySuburb |
P41355
|
FINISHED |
| Object |
Rosanna
Rosanna is a residential suburb in Melbourne, Australia, known for its leafy streets, family-friendly atmosphere, and proximity to parklands and public transport.
|
E1063733
|
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: Rosanna | Statement: [Heidelberg, nearbySuburb, Rosanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosanna Context triple: [Heidelberg, nearbySuburb, Rosanna]
-
A.
Rosanna
Rosanna is a feminine given name of Latin origin, derived from a combination of "Rose" and "Anna."
-
B.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
C.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
-
D.
Carmen Luna
Carmen Luna is a fiercely ambitious and witty Latina maid and aspiring singer who navigates love, class, and career struggles in the TV series "Devious Maids."
-
E.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
- 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: Rosanna Triple: [Heidelberg, nearbySuburb, Rosanna]
Generated description
Rosanna is a residential suburb in Melbourne, Australia, known for its leafy streets, family-friendly atmosphere, and proximity to parklands and public transport.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rosanna Target entity description: Rosanna is a residential suburb in Melbourne, Australia, known for its leafy streets, family-friendly atmosphere, and proximity to parklands and public transport.
-
A.
Rosanna
Rosanna is a feminine given name of Latin origin, derived from a combination of "Rose" and "Anna."
-
B.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
C.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
-
D.
Carmen Luna
Carmen Luna is a fiercely ambitious and witty Latina maid and aspiring singer who navigates love, class, and career struggles in the TV series "Devious Maids."
-
E.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0295d2d48190b08eba0d805bd72d |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8e85c6c81908bdf5d43b917d151 |
completed | May 3, 2026, 9:06 p.m. |
| NEDg | Description generation | batch_69f7b9d81f488190875e9b3f885556ab |
completed | May 3, 2026, 9:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7ba99ad9c8190906b6b63cf27a446 |
completed | May 3, 2026, 9:14 p.m. |
Created at: April 9, 2026, 10:13 p.m.