Triple

T26128111
Position Surface form Disambiguated ID Type / Status
Subject Interior Characterization of Europa using Magnetometry E659159 entity
Predicate shortName P43 FINISHED
Object ICEMAG
ICEMAG is a planned magnetometer instrument designed to study the interior structure and subsurface ocean of Jupiter’s moon Europa by measuring its magnetic field.
E1710796 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: ICEMAG | Statement: [Interior Characterization of Europa using Magnetometry, shortName, ICEMAG]
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: ICEMAG
Triple: [Interior Characterization of Europa using Magnetometry, shortName, ICEMAG]
Generated description
ICEMAG is a planned magnetometer instrument designed to study the interior structure and subsurface ocean of Jupiter’s moon Europa by measuring its magnetic field.

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_69ee5bc2b2948190b458ad3f580af779 completed April 26, 2026, 6:38 p.m.
NER Named-entity recognition batch_69f60b90dccc8190a84fa77d8243648f completed May 2, 2026, 2:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11275f50348190a4228394659eb00b completed May 23, 2026, 4:04 a.m.
NEDg Description generation batch_6a1136528708819080183faa89fe9eb2 completed May 23, 2026, 5:08 a.m.
NED2 Entity disambiguation (via description) batch_6a1136aa7534819091b4c2bea17aacc3 completed May 23, 2026, 5:10 a.m.
Created at: April 26, 2026, 8:13 p.m.