Triple

T18198888
Position Surface form Disambiguated ID Type / Status
Subject ESPaDOnS E435729 entity
Predicate dataPipeline P28421 FINISHED
Object UPENA
UPENA is the dedicated data reduction and processing pipeline used to handle and analyze observations from the ESPaDOnS spectropolarimeter.
 E1311768 NE FINISHED

How this triple was built (4 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: UPENA | Statement: [ESPaDOnS, dataPipeline, UPENA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UPENA
Context triple: [ESPaDOnS, dataPipeline, UPENA]
  • A. Opañel
    Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
  • B. Penkun
    Penkun is a small historic town in northeastern Germany, located near the Polish border in the state of Mecklenburg-Vorpommern.
  • C. Upata
    Upata is a town in southeastern Venezuela known as an agricultural and commercial center within Bolívar State.
  • D. Ōpunake
    Ōpunake is a small coastal town on the west coast of New Zealand’s North Island, known for its surf beach and views of Mount Taranaki.
  • E. Utapaun
    Utapaun are a humanoid species from the sinkhole world of Utapau in the Star Wars universe, known for their distinctive facial markings and deeply lined features.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: UPENA
Triple: [ESPaDOnS, dataPipeline, UPENA]
Generated description
UPENA is the dedicated data reduction and processing pipeline used to handle and analyze observations from the ESPaDOnS spectropolarimeter.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UPENA
Target entity description: UPENA is the dedicated data reduction and processing pipeline used to handle and analyze observations from the ESPaDOnS spectropolarimeter.
  • A. Opañel
    Opañel is a Madrid Metro station serving the Carabanchel district in Spain.
  • B. Penkun
    Penkun is a small historic town in northeastern Germany, located near the Polish border in the state of Mecklenburg-Vorpommern.
  • C. Upata
    Upata is a town in southeastern Venezuela known as an agricultural and commercial center within Bolívar State.
  • D. Ōpunake
    Ōpunake is a small coastal town on the west coast of New Zealand’s North Island, known for its surf beach and views of Mount Taranaki.
  • E. Utapaun
    Utapaun are a humanoid species from the sinkhole world of Utapau in the Star Wars universe, known for their distinctive facial markings and deeply lined features.
  • F. None of above. chosen

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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e0d545f4819090285d1446bd3c27 completed April 19, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03980f600c819099f3fb14744f8afa completed May 12, 2026, 9:13 p.m.
NEDg Description generation batch_6a0398fb7fb481908a32c56a798d81fd completed May 12, 2026, 9:17 p.m.
NED2 Entity disambiguation (via description) batch_6a039a2b85888190bbcae1ba9d8ba715 completed May 12, 2026, 9:22 p.m.
Created at: April 10, 2026, 10:31 a.m.