Triple
T1124548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louis Dieudonné |
E24688
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Racine |
E91753
|
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: Racine | Statement: [Louis Dieudonné, employer, Racine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Racine Context triple: [Louis Dieudonné, employer, Racine]
-
A.
Racine
chosen
Racine is a city in southeastern Wisconsin located on the shore of Lake Michigan, known historically for its manufacturing industry and Danish kringle pastries.
-
B.
Kenosha
Kenosha is a mid-sized city in southeastern Wisconsin located on the shore of Lake Michigan between Milwaukee and Chicago.
-
C.
Milwaukie
Milwaukie is a small city in northwestern Oregon, located just south of Portland along the Willamette River.
-
D.
Milwaukee
Milwaukee is the largest city in Wisconsin, known for its brewing traditions, industrial history, and location on the western shore of Lake Michigan.
-
E.
Fort Madison
Fort Madison is a historic riverfront city in southeastern Iowa known for its Mississippi River port, 19th-century military fort heritage, and role as a regional transportation hub.
- 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_69a4940712c88190aa244f3fc6070a65 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bbd92a8c8190a16e55f3f739010f |
completed | March 1, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac667127008190b2aa1f3aafc87340 |
completed | March 7, 2026, 5:54 p.m. |
Created at: March 1, 2026, 7:44 p.m.