Triple
T14578671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nannette Streicher |
E342126
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Anna-Maria
Anna-Maria is the given name of Nannette Streicher, a noted Viennese piano maker and close associate of Ludwig van Beethoven.
|
E1105878
|
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: Anna-Maria | Statement: [Nannette Streicher, givenName, Anna-Maria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna-Maria Context triple: [Nannette Streicher, givenName, Anna-Maria]
-
A.
Anna Margareta
Anna Margareta Tunder was a historical figure known primarily as the namesake and likely relative of the German Baroque composer and organist Franz Tunder.
-
B.
Anna Marie
Anna Marie, better known as Rogue, is a popular Marvel Comics superhero and longtime member of the X-Men who absorbs others’ powers and memories through touch.
-
C.
Ottilia
Ottilia is a feminine given name of Germanic origin, related to Otto and typically interpreted to mean "wealth" or "prosperity."
-
D.
Johanna
Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
-
E.
Johanna
"Johanna" is a recurring, lyrically poignant love song from Stephen Sondheim's musical *Sweeney Todd: The Demon Barber of Fleet Street*.
- 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: Anna-Maria Triple: [Nannette Streicher, givenName, Anna-Maria]
Generated description
Anna-Maria is the given name of Nannette Streicher, a noted Viennese piano maker and close associate of Ludwig van Beethoven.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anna-Maria Target entity description: Anna-Maria is the given name of Nannette Streicher, a noted Viennese piano maker and close associate of Ludwig van Beethoven.
-
A.
Anna Margareta
Anna Margareta Tunder was a historical figure known primarily as the namesake and likely relative of the German Baroque composer and organist Franz Tunder.
-
B.
Anna Marie
Anna Marie, better known as Rogue, is a popular Marvel Comics superhero and longtime member of the X-Men who absorbs others’ powers and memories through touch.
-
C.
Ottilia
Ottilia is a feminine given name of Germanic origin, related to Otto and typically interpreted to mean "wealth" or "prosperity."
-
D.
Johanna
Johanna is the given name of Johanna Spyri, the Swiss author best known for creating the classic children's novel "Heidi."
-
E.
Johanna
"Johanna" is a recurring, lyrically poignant love song from Stephen Sondheim's musical *Sweeney Todd: The Demon Barber of Fleet Street*.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb3f6f78c81908a30ecb4c025299d |
completed | April 14, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ad03e7881908a783182c6d656b5 |
completed | May 8, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_69fd8c278e8481909465972b32ad6c28 |
completed | May 8, 2026, 7:09 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8ca921108190943eb948f9af6123 |
completed | May 8, 2026, 7:11 a.m. |
Created at: April 10, 2026, 1:24 a.m.