Triple
T17607820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peter Bowker |
E428880
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | Monroe |
—
|
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: Monroe | Statement: [Peter Bowker, wrote, Monroe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monroe Context triple: [Peter Bowker, wrote, Monroe]
-
A.
Monroe
Monroe is a city in southeastern Michigan known for its location along the River Raisin and its historical significance in the War of 1812.
-
B.
Monroe
chosen
Monroe is a British medical drama television series centered on a brilliant but troubled neurosurgeon, starring James Nesbitt in the title role.
-
C.
Monroe
Monroe is the young boy protagonist of the surreal first-person adventure game "The Unfinished Swan," known for exploring a mysterious, mostly blank world with a magical paint-throwing mechanic.
-
D.
Monroe
Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
-
E.
Monroe
Monroe is a small city in Washington State known for its location in the Skykomish River Valley and its role as a regional hub for outdoor recreation and community events.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4d8374819097cb112fba405e77 |
completed | April 19, 2026, 5:46 a.m. |
Created at: April 10, 2026, 5:51 a.m.