Triple
T2560259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camm |
E57224
|
entity |
| Predicate | notableBearer |
P458
|
FINISHED |
| Object | Sydney Camm |
E7252
|
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: Sydney Camm | Statement: [Camm, notableBearer, Sydney Camm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sydney Camm Context triple: [Camm, notableBearer, Sydney Camm]
-
A.
Sydney Camm
chosen
Sydney Camm was a British aircraft designer best known for creating the Hawker Hurricane, one of the Royal Air Force’s key fighter planes during World War II.
-
B.
Rachel Kempson
Rachel Kempson was an English actress and matriarch of the Redgrave acting family, known for her extensive stage and film career.
-
C.
Michele Timms
Michele Timms is a pioneering Australian basketball point guard renowned for her international success and trailblazing role in women’s professional basketball.
-
D.
Philip Neame
Philip Neame was a British Army officer and Victoria Cross recipient who rose to the rank of general and held key commands in the North African campaign during the Second World War.
-
E.
Neil Pearson
Neil Pearson is a British actor known for his work in television, film, and theatre, including prominent roles in series like "Drop the Dead Donkey" and "Between the Lines."
- 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_69ab4a4ef9008190a0e6d4422b9418b7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd334b69481908a6f2c0b41550ce9 |
completed | March 7, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d233dbc81909feb1127cffb027f |
completed | March 9, 2026, 11:52 p.m. |
Created at: March 6, 2026, 9:48 p.m.