Triple
T19241412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monroe Bowers Morton |
E481140
|
entity |
| Predicate | givenName |
P17
|
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: [Monroe Bowers Morton, givenName, Monroe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monroe Context triple: [Monroe Bowers Morton, givenName, Monroe]
-
A.
Monroe
Monroe is a Chicago 'L' rapid transit station located in the Loop and served by the Chicago Transit Authority's Red Line.
-
B.
Monroe
Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
-
C.
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.
-
D.
Monroe
chosen
Monroe is a given name used as a first name, notably borne by actor Jackson Rathbone.
-
E.
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.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faf14564819085009adf822804ab |
completed | April 20, 2026, 10:07 a.m. |
Created at: April 10, 2026, 1:26 p.m.