Triple

T26518811
Position Surface form Disambiguated ID Type / Status
Subject VLT instrumentation suite E669894 entity
Predicate supportsTelescope P12724 FINISHED
Object VLT Unit Telescope 2 Kueyen
VLT Unit Telescope 2 Kueyen is one of the four 8.2-meter Very Large Telescope units at ESO’s Paranal Observatory in Chile, used for advanced optical and near-infrared astronomical observations.
 E1739300 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: VLT Unit Telescope 2 Kueyen | Statement: [VLT instrumentation suite, supportsTelescope, VLT Unit Telescope 2 Kueyen]
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: VLT Unit Telescope 2 Kueyen
Triple: [VLT instrumentation suite, supportsTelescope, VLT Unit Telescope 2 Kueyen]
Generated description
VLT Unit Telescope 2 Kueyen is one of the four 8.2-meter Very Large Telescope units at ESO’s Paranal Observatory in Chile, used for advanced optical and near-infrared astronomical observations.

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_69eeb31b6dcc8190b30632dc3928a0c0 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f613c06530819095609b53dda121b4 completed May 2, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11fe5998a48190a64204bfd9647424 completed May 23, 2026, 7:22 p.m.
NEDg Description generation batch_6a1202266b8081908e713da51627ff49 completed May 23, 2026, 7:38 p.m.
NED2 Entity disambiguation (via description) batch_6a120276c67c819083ed964da42690e1 completed May 23, 2026, 7:39 p.m.
Created at: April 27, 2026, 1:26 a.m.