Triple
T648979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osaka Evessa |
E11302
|
entity |
| Predicate | hasRival |
P1375
|
FINISHED |
| Object |
Shiga Lakes
Shiga Lakes is a professional basketball team based in Shiga Prefecture, Japan, competing in the country’s top-tier B.League.
|
E87249
|
NE FINISHED |
How this triple was built (4 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: Shiga Lakes | Statement: [Osaka Evessa, hasRival, Shiga Lakes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiga Lakes Context triple: [Osaka Evessa, hasRival, Shiga Lakes]
-
A.
Simly Lake
Simly Lake is a major freshwater reservoir and popular recreational spot located in the Margalla Hills near Islamabad, Pakistan.
-
B.
Benya Lagoon
Benya Lagoon is a coastal lagoon in Elmina, Ghana, known for its fishing activities and proximity to the historic Elmina Castle.
-
C.
Tonetta Lake
Tonetta Lake is a small hamlet in the Town of Southeast in Putnam County, New York, known for its residential community centered around the nearby lake.
-
D.
Lake Kegonsa
Lake Kegonsa is a glacial freshwater lake in south-central Wisconsin that is popular for boating, fishing, and its surrounding state park.
-
E.
Ancylus Lake
Ancylus Lake was a large freshwater body that occupied the Baltic Sea basin after the last Ice Age, before it evolved into the modern brackish Baltic Sea.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Shiga Lakes Triple: [Osaka Evessa, hasRival, Shiga Lakes]
Generated description
Shiga Lakes is a professional basketball team based in Shiga Prefecture, Japan, competing in the country’s top-tier B.League.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shiga Lakes Target entity description: Shiga Lakes is a professional basketball team based in Shiga Prefecture, Japan, competing in the country’s top-tier B.League.
-
A.
Simly Lake
Simly Lake is a major freshwater reservoir and popular recreational spot located in the Margalla Hills near Islamabad, Pakistan.
-
B.
Benya Lagoon
Benya Lagoon is a coastal lagoon in Elmina, Ghana, known for its fishing activities and proximity to the historic Elmina Castle.
-
C.
Tonetta Lake
Tonetta Lake is a small hamlet in the Town of Southeast in Putnam County, New York, known for its residential community centered around the nearby lake.
-
D.
Lake Kegonsa
Lake Kegonsa is a glacial freshwater lake in south-central Wisconsin that is popular for boating, fishing, and its surrounding state park.
-
E.
Ancylus Lake
Ancylus Lake was a large freshwater body that occupied the Baltic Sea basin after the last Ice Age, before it evolved into the modern brackish Baltic Sea.
- F. None of above. chosen
Provenance (5 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f308f34819094ba28cfc786051e |
completed | March 1, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a64a4d77e0819098cdd416136fd374 |
completed | March 3, 2026, 2:41 a.m. |
| NEDg | Description generation | batch_69a64af942008190b1de8991f642f32c |
completed | March 3, 2026, 2:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a64b80d5fc81909e69832457569064 |
completed | March 3, 2026, 2:46 a.m. |
Created at: March 1, 2026, 7:36 p.m.