Triple
T21959257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Hearne |
E542279
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | John Hearne |
—
|
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: John Hearne | Statement: [John Hearne, name, John Hearne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Hearne Context triple: [John Hearne, name, John Hearne]
-
A.
John Hearne
chosen
John Hearne was an Irish constitutional lawyer and civil servant best known as the principal architect of the 1937 Constitution of Ireland.
-
B.
Stephen Healey
Stephen Healey is an individual notable enough to be recognized as a namesake of the surname Healey.
-
C.
Andrew Pyle
Andrew Pyle is a British philosopher known for his work in the philosophy of science, metaphysics, and the history of early modern philosophy.
-
D.
Samuel Hynes
Samuel Hynes was an American literary critic, World War II veteran, and Princeton professor best known for his influential writings on war literature and the cultural history of modern conflict.
-
E.
Les Healey
Les Healey is a film editor best known for his work on the science fiction classic "Blade Runner."
- 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_69e0c47fab1081908dc74a6545dbb051 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12454a290819094d4b56547816e3f |
completed | April 28, 2026, 9:19 p.m. |
Created at: April 16, 2026, 8 p.m.