Triple

T15745637
Position Surface form Disambiguated ID Type / Status
Subject Kenya–Tanzania border E381714 entity
Predicate passesNear P416 FINISHED
Object Lake Chala E1167471 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 Chala | Statement: [Kenya–Tanzania border, passesNear, Lake Chala]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Chala
Context triple: [Kenya–Tanzania border, passesNear, Lake Chala]
  • A. Lake Chala chosen
    Lake Chala is a crater lake on the border between Kenya and Tanzania, renowned for its steep volcanic caldera, clear turquoise waters, and rich biodiversity.
  • B. Lake Eyasi
    Lake Eyasi is a shallow, seasonal saline lake in northern Tanzania, known for its remote setting near the Serengeti and as home to the Hadza hunter-gatherer people.
  • C. Udziro Lake
    Udziro Lake is a high-altitude alpine lake in Georgia’s Racha region, renowned for its striking blue waters and panoramic views of the surrounding Caucasus Mountains.
  • D. Matemale Lake
    Matemale Lake is a large artificial reservoir in the Capcir region of the French Pyrenees, popular for outdoor recreation such as hiking, sailing, and fishing.
  • E. Lake Zug
    Lake Zug is a picturesque glacial lake in central Switzerland, known for its scenic alpine surroundings, mild climate, and lakeside towns such as Zug and Arth.
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0502c0c3c8190b8e512df307039c1 completed April 16, 2026, 2:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8307824881909ba85e4c3da65d28 completed May 9, 2026, 6:55 p.m.
Created at: April 10, 2026, 4:46 a.m.