Triple
T1034941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | James Monroe |
E22339
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Monroe |
E115349
|
NE FINISHED |
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: [James Monroe, familyName, Monroe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monroe Context triple: [James Monroe, familyName, 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
chosen
Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
-
C.
Madison
Madison is a common English surname and given name, historically associated with U.S. President James Madison and now widely used as a first name, especially for girls.
-
D.
Madison
Madison is a suburban city in northern Alabama known for its proximity to Huntsville and its strong schools and residential communities.
-
E.
Canton
Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b814c16c8190ac4d20feecdadbae |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac763827b08190b84cc18a5cfa64fc |
completed | March 7, 2026, 7:02 p.m. |
Created at: March 1, 2026, 7:41 p.m.