Triple

T30830792
Position Surface form Disambiguated ID Type / Status
Subject Val Cavallina E785209 entity
Predicate hasPart P35 FINISHED
Object Lake Endine
Lake Endine is a small, scenic alpine lake in northern Italy’s Lombardy region, known for its tranquil waters, surrounding hills, and popularity for fishing and outdoor recreation.
 E2294086 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 Endine | Statement: [Val Cavallina, hasPart, Lake Endine]
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 Endine
Triple: [Val Cavallina, hasPart, Lake Endine]
Generated description
Lake Endine is a small, scenic alpine lake in northern Italy’s Lombardy region, known for its tranquil waters, surrounding hills, and popularity for fishing and outdoor recreation.

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_69f224b6642481909e8d701de2cd1a53 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f690f9b8ac8190b5913fffaae48346 completed May 3, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a7b766055748190b43fbc57cde9e032 completed Aug. 11, 2026, 7:22 p.m.
NEDg Description generation batch_6a7b76afac088190b8a45c3c8f274058 completed Aug. 11, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_6a7b77441b0c8190a3e5f3072a522eac completed Aug. 11, 2026, 7:25 p.m.
Created at: April 29, 2026, 8:44 p.m.