Triple
T21316101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avro Tutor |
E525473
|
entity |
| Predicate | developedBy |
P73
|
FINISHED |
| Object | Avro company |
—
|
NE NERFINISHED |
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 company | Statement: [Avro Tutor, developedBy, Avro company]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Avro company Context triple: [Avro Tutor, developedBy, Avro company]
-
A.
Avro Ltd
Avro Ltd was a pioneering British aircraft manufacturer best known for designing iconic military aircraft such as the Avro Lancaster bomber.
-
B.
Avro
Avro is a row-oriented, schema-based data serialization format commonly used in big data processing and storage systems.
-
C.
Avro
chosen
Avro was a British aircraft manufacturer best known for producing iconic military aircraft such as the Avro Lancaster bomber during the 20th century.
-
D.
British Aircraft Corporation
British Aircraft Corporation was a major British aerospace manufacturer formed in 1960, known for producing military and civil aircraft and as a key partner in international projects like the Concorde.
-
E.
Airspeed Ltd
Airspeed Ltd was a British aircraft manufacturer known for producing civil and military airplanes in the mid-20th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b51ad810819098c12392c8e55f6c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75dcfdec08190a6e19c907a544921 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 16, 2026, 4:29 p.m.