Triple
T6520555
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MECCA Arena |
E148367
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Milwaukee |
E10031
|
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: Milwaukee | Statement: [MECCA Arena, city, Milwaukee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Milwaukee Context triple: [MECCA Arena, city, Milwaukee]
-
A.
Milwaukee
chosen
Milwaukee is the largest city in Wisconsin, known for its brewing traditions, industrial history, and location on the western shore of Lake Michigan.
-
B.
Milwaukie
Milwaukie is a small city in northwestern Oregon, located just south of Portland along the Willamette River.
-
C.
Kenosha
Kenosha is a mid-sized city in southeastern Wisconsin located on the shore of Lake Michigan between Milwaukee and Chicago.
-
D.
Waukesha, Wisconsin
Waukesha, Wisconsin is a suburban city west of Milwaukee known for its historic downtown, former mineral springs resorts, and location along the Fox River.
-
E.
Stevens Point
Stevens Point is a small city in central Wisconsin known for its university, historic downtown, and access to outdoor recreation along the Wisconsin River.
- 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_69c687e68e748190baceb9298f32d3ed |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ad92c624819086dbb12b4f6b78d3 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d506900c819093e9528426875942 |
completed | March 27, 2026, 7:05 p.m. |
Created at: March 27, 2026, 1:45 p.m.