Triple
T19560318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trevor Penick |
E489431
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Trevor |
—
|
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: Trevor | Statement: [Trevor Penick, givenName, Trevor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trevor Context triple: [Trevor Penick, givenName, Trevor]
-
A.
Trevor
chosen
Trevor is a masculine given name of English origin commonly used in the UK and other English-speaking countries.
-
B.
Trevor
Trevor is a village in Wrexham County Borough, Wales, known for its proximity to the UNESCO-listed Pontcysyllte Aqueduct on the Llangollen Canal.
-
C.
Trevor Jim
Trevor Jim was a computer scientist and cryptographer known for his work on programming languages, security, and formal methods.
-
D.
Trevor Steele
Trevor Steele is an Australian writer best known for his significant contributions to modern Esperanto literature.
-
E.
Trevor Darrell
Trevor Darrell is a prominent computer vision and machine learning researcher and professor known for his work on deep learning, visual recognition, and autonomous systems.
- 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63f731ae48190ade295c15db7f8ed |
completed | April 20, 2026, 3 p.m. |
Created at: April 10, 2026, 1:42 p.m.