Triple

T27103456
Position Surface form Disambiguated ID Type / Status
Subject Kerang E686500 entity
Predicate near P350 FINISHED
Object Lake Tutchewop
Lake Tutchewop is a shallow, saline lake in northern Victoria, Australia, known for its distinctive pink coloration and birdlife habitat.
E2297233 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 Tutchewop | Statement: [Kerang, near, Lake Tutchewop]
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: Lake Tutchewop
Triple: [Kerang, near, Lake Tutchewop]
Generated description
Lake Tutchewop is a shallow, saline lake in northern Victoria, Australia, known for its distinctive pink coloration and birdlife habitat.

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_69ef1489f8b481908e24a1985982bd26 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623b855ac81909a5c7c286a44ec89 completed May 2, 2026, 4:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a8336470f1081909f9611f97b408f7c completed Aug. 17, 2026, 4:26 p.m.
NEDg Description generation batch_6a8336ee86b48190972b0155fb2e78ef completed Aug. 17, 2026, 4:29 p.m.
NED2 Entity disambiguation (via description) batch_6a83371bfe8481908b8110013f6debee completed Aug. 17, 2026, 4:30 p.m.
Created at: April 27, 2026, 8:49 a.m.