Triple

T4793154
Position Surface form Disambiguated ID Type / Status
Subject Thomas Ruff E106649 entity
Predicate seriesCreated P5039 FINISHED
Object ma.r.s
ma.r.s is a photographic series by German artist Thomas Ruff that transforms satellite imagery of the Martian surface into large-scale, abstracted landscape works.
 E468492 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: ma.r.s | Statement: [Thomas Ruff, seriesCreated, ma.r.s]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ma.r.s
Context triple: [Thomas Ruff, seriesCreated, ma.r.s]
  • A. MRS
    MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
  • B. MAR
    MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
  • C. MAS
    MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
  • D. MAS
    MAS (Monetary Authority of Singapore) is Singapore’s central bank and integrated financial regulator, responsible for monetary policy, financial supervision, and the stability of the country’s financial system.
  • E. MAS
    MAS is the official Indian Railways station code for Chennai Central, one of the busiest and most important railway terminals in South India.
  • 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: ma.r.s
Triple: [Thomas Ruff, seriesCreated, ma.r.s]
Generated description
ma.r.s is a photographic series by German artist Thomas Ruff that transforms satellite imagery of the Martian surface into large-scale, abstracted landscape works.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ma.r.s
Target entity description: ma.r.s is a photographic series by German artist Thomas Ruff that transforms satellite imagery of the Martian surface into large-scale, abstracted landscape works.
  • A. MRS
    MRS is the Materials Research Society, a professional organization dedicated to advancing interdisciplinary materials science and engineering research and education.
  • B. MAR
    MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
  • C. MAS
    MAS is the official Indian Railways station code for Chennai Central, one of the busiest and most important railway terminals in South India.
  • D. MAS
    MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
  • E. MAS
    MAS (Monetary Authority of Singapore) is Singapore’s central bank and integrated financial regulator, responsible for monetary policy, financial supervision, and the stability of the country’s financial system.
  • 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_69bd43f591c881909e5a532388b0f3f3 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd66072ebc8190ae0e6cef1e7b07e4 completed March 20, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69be43f0be108190aebcc9b1a824e624 completed March 21, 2026, 7:08 a.m.
NEDg Description generation batch_69be44b374a081908862ebad48332d16 completed March 21, 2026, 7:11 a.m.
NED2 Entity disambiguation (via description) batch_69be4530fe5c8190aa976151cdd5bc7a completed March 21, 2026, 7:13 a.m.
Created at: March 20, 2026, 1:22 p.m.