Triple

T24757798
Position Surface form Disambiguated ID Type / Status
Subject Kilivila language E619339 entity
Predicate documentedBy P4310 FINISHED
Object Gunter Senft
Gunter Senft is a German linguist and anthropologist known for his extensive fieldwork and influential studies on the Austronesian languages and cultures of the Trobriand Islands.
E2063827 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: Gunter Senft | Statement: [Kilivila language, documentedBy, Gunter Senft]
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: Gunter Senft
Triple: [Kilivila language, documentedBy, Gunter Senft]
Generated description
Gunter Senft is a German linguist and anthropologist known for his extensive fieldwork and influential studies on the Austronesian languages and cultures of the Trobriand Islands.

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_69e2fabbea94819092ed41348909622f completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f41079bfe4819084d0477ee670b60e completed May 1, 2026, 2:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a363c6a7d8c81909089eab80a8aaa46 completed June 20, 2026, 7:08 a.m.
NEDg Description generation batch_6a364ba0f2a08190a4a52f9d32829a9f completed June 20, 2026, 8:13 a.m.
NED2 Entity disambiguation (via description) batch_6a364c67a6bc8190a2c149406910a337 completed June 20, 2026, 8:16 a.m.
Created at: April 18, 2026, 4:26 a.m.