Triple
T14484718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madison |
E359196
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object | Lake Wingra |
E69364
|
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: Lake Wingra | Statement: [Madison, hasLake, Lake Wingra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Wingra Context triple: [Madison, hasLake, Lake Wingra]
-
A.
Lake Wingra
chosen
Lake Wingra is a small urban lake in Madison, Wisconsin, known for its surrounding parks, wildlife habitat, and recreational activities like paddling and fishing.
-
B.
Stadtsee
Stadtsee is a small lake located in the town of Bad Waldsee in southern Germany, known for its scenic setting and recreational use.
-
C.
Lake Heiligensee
Lake Heiligensee is a small freshwater lake in the Heiligensee district of Berlin, Germany, known for its recreational use and scenic natural surroundings.
-
D.
Muldestausee
Muldestausee is a municipality in the district of Anhalt-Bitterfeld in Saxony-Anhalt, Germany, known for the large Mulde reservoir and its surrounding natural and recreational areas.
-
E.
Schlachtensee
Schlachtensee is a lake and popular recreational area in southwestern Berlin, known for swimming, walking trails, and its surrounding forested landscape.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de924d7f4c8190b1f62b5ffe1ff649 |
completed | April 14, 2026, 7:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64a73cf48190811d6de182e891c4 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 10, 2026, 1:20 a.m.