Triple
T38599113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria Anna Mozart |
E934161
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Mozart
Mozart is the surname of the famous Austrian musical family that includes composer Wolfgang Amadeus Mozart and his sister Maria Anna.
|
E2277883
|
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: Mozart | Statement: [Maria Anna Mozart, familyName, Mozart]
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: Mozart Triple: [Maria Anna Mozart, familyName, Mozart]
Generated description
Mozart is the surname of the famous Austrian musical family that includes composer Wolfgang Amadeus Mozart and his sister Maria Anna.
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_69f76ecc17688190b389b693a5927501 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd952ecf48190bf1e524c16da3dcc |
completed | May 7, 2026, 6:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41f43d6f7081908116fc6d4f1f39c6 |
completed | June 29, 2026, 4:27 a.m. |
| NEDg | Description generation | batch_6a41f510ea6481908491ca410f6f7ec3 |
completed | June 29, 2026, 4:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41f61ba5a08190b74a5c3ec29c3665 |
completed | June 29, 2026, 4:35 a.m. |
Created at: May 3, 2026, 4:32 p.m.