Triple
T7984913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azure Data Lake Storage |
E185662
|
entity |
| Predicate | supportsFormat |
P203
|
FINISHED |
| Object | Avro |
E102384
|
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: Avro | Statement: [Azure Data Lake Storage, supportsFormat, Avro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Avro Context triple: [Azure Data Lake Storage, supportsFormat, Avro]
-
A.
Avro
chosen
Avro is a row-oriented, schema-based data serialization format commonly used in big data processing and storage systems.
-
B.
Avro
Avro was a British aircraft manufacturer best known for producing iconic military aircraft such as the Avro Lancaster bomber during the 20th century.
-
C.
Hawker
Hawker is a surname of English origin borne by various notable individuals across fields such as aviation, politics, and the arts.
-
D.
Avro Athena
The Avro Athena was a British postwar advanced trainer aircraft developed by Avro for the Royal Air Force.
-
E.
de Havilland
De Havilland is the distinguished Anglo-French family name shared by Hollywood actresses Joan Fontaine and her sister Olivia de Havilland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4a55b881909a96133e56c0dffa |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe0e0b2748190930c22c6157d1b07 |
completed | March 31, 2026, 2:57 p.m. |
Created at: March 30, 2026, 5:15 p.m.