Triple
T20814462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lovelock |
E512399
|
entity |
| Predicate | artist |
P184
|
FINISHED |
| Object | Gene Page |
—
|
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: Gene Page | Statement: [Lovelock, artist, Gene Page]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gene Page Context triple: [Lovelock, artist, Gene Page]
-
A.
Gene Page
chosen
Gene Page was an American arranger, producer, and conductor best known for his lush orchestral work on numerous soul, R&B, and pop recordings from the 1960s through the 1980s.
-
B.
Stanley Warnow
Stanley Warnow is a film editor best known for his work on movies such as the 1979 musical comedy "Hair."
-
C.
John C. Avise
John C. Avise is an American evolutionary geneticist renowned for pioneering the use of molecular markers to study natural populations, phylogeography, and conservation biology.
-
D.
Ian Darwin
Ian Darwin is a software developer and author best known for his contributions to Unix, Java, and open source programming resources.
-
E.
Sean B. Carroll
Sean B. Carroll is an evolutionary biologist, author, and science communicator known for his work in evolutionary developmental biology and for popularizing science through award-winning books and media.
- 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2d4e43c8190aecce82a3f7e2de0 |
completed | April 21, 2026, 12:20 a.m. |
Created at: April 16, 2026, 12:41 p.m.