Triple
T19810317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinus jeffreyi |
E475925
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | John Jeffrey |
—
|
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 Jeffrey | Statement: [Pinus jeffreyi, namedAfter, John Jeffrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Jeffrey Context triple: [Pinus jeffreyi, namedAfter, John Jeffrey]
-
A.
John Jeffrey
chosen
John Jeffrey was a 19th-century Scottish botanist and plant collector known for his explorations in western North America, where he documented and introduced numerous conifer species.
-
B.
David Jeffrey
David Jeffrey is a highly successful Northern Irish football manager and former player, best known for his trophy-laden spell in charge of Linfield FC.
-
C.
Jeffrey Winston
Jeffrey Winston is known as the former husband of American actress Debbi Morgan.
-
D.
Jeffrey Lynn
Jeffrey Lynn was an American film and stage actor best known for his roles in 1930s and 1940s Hollywood dramas and romances.
-
E.
Jeffrey Byron
Jeffrey Byron is an American actor known for his work in film and television since the 1960s, including roles in genre and action productions.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6542bd7a48190acf67db41f1131c9 |
completed | April 20, 2026, 4:28 p.m. |
Created at: April 10, 2026, 1:50 p.m.