Triple
T23817958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beriev Aircraft Company |
E589146
|
entity |
| Predicate | notableProduct |
P1448
|
FINISHED |
| Object |
Beriev A-40 Albatros
The Beriev A-40 Albatros is a large Soviet/Russian jet-powered amphibious aircraft designed primarily for anti-submarine warfare and maritime patrol duties.
|
E1629100
|
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: Beriev A-40 Albatros | Statement: [Beriev Aircraft Company, notableProduct, Beriev A-40 Albatros]
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: Beriev A-40 Albatros Triple: [Beriev Aircraft Company, notableProduct, Beriev A-40 Albatros]
Generated description
The Beriev A-40 Albatros is a large Soviet/Russian jet-powered amphibious aircraft designed primarily for anti-submarine warfare and maritime patrol duties.
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_69e25d18619081909c7fb89d8926f14a |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c7ac0c688190a627b9b42c7587d5 |
completed | April 29, 2026, 8:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fc9911b24819087c34f44859e1831 |
completed | May 22, 2026, 3:12 a.m. |
| NEDg | Description generation | batch_6a0fcd4eb78c81909dacbe3f7e43dd43 |
completed | May 22, 2026, 3:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fcf9f90cc8190a14c8d2e9ef982a4 |
completed | May 22, 2026, 3:38 a.m. |
Created at: April 17, 2026, 7:58 p.m.