Triple
T37493181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orchestral Suite No. 4 in D major, BWV 1069 |
E931752
|
entity |
| Predicate | catalogNumber |
P8090
|
FINISHED |
| Object |
BWV 1069
BWV 1069 is Johann Sebastian Bach’s Orchestral Suite No. 4 in D major, a festive Baroque orchestral work noted for its grand overture and lively dance movements.
|
E2236428
|
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: BWV 1069 | Statement: [Orchestral Suite No. 4 in D major, BWV 1069, catalogNumber, BWV 1069]
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: BWV 1069 Triple: [Orchestral Suite No. 4 in D major, BWV 1069, catalogNumber, BWV 1069]
Generated description
BWV 1069 is Johann Sebastian Bach’s Orchestral Suite No. 4 in D major, a festive Baroque orchestral work noted for its grand overture and lively dance movements.
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_69f76ec457a4819094eeb3aed9baac11 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba37cebe08190a8bd035faca7dc13 |
completed | May 6, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40afd082848190b9369602c32d4eaa |
completed | June 28, 2026, 5:23 a.m. |
| NEDg | Description generation | batch_6a40b28343e8819081b5ec941e7f11fa |
completed | June 28, 2026, 5:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a40b2c2a0bc8190a89aa440a286acc1 |
completed | June 28, 2026, 5:36 a.m. |
Created at: May 3, 2026, 4:17 p.m.