Triple

T9566689
Position Surface form Disambiguated ID Type / Status
Subject Standard ML E230804 entity
Predicate hasImplementation P3697 FINISHED
Object MLton
MLton is a whole-program optimizing compiler for the Standard ML programming language, known for its aggressive optimizations and high-performance native code generation.
 E807595 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: MLton | Statement: [Standard ML, hasImplementation, MLton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MLton
Context triple: [Standard ML, hasImplementation, MLton]
  • A. MLT
    MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
  • B. Talbo
    Talbo is the surname of Dolly Talbo, a character whose last name identifies her within her fictional or narrative family lineage.
  • C. Maltoni
    Maltoni is the Italian maiden surname of Rosa Maltoni Mussolini, the mother of fascist dictator Benito Mussolini.
  • D. Mattertal
    Mattertal is a high alpine valley in the Swiss canton of Valais, known for its dramatic peaks including the Matterhorn and its popular mountaineering and ski resorts.
  • E. Molten
    Molten is a Japanese sports equipment manufacturer best known for producing high-quality balls used in major international competitions across football, basketball, and other sports.
  • 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: MLton
Triple: [Standard ML, hasImplementation, MLton]
Generated description
MLton is a whole-program optimizing compiler for the Standard ML programming language, known for its aggressive optimizations and high-performance native code generation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MLton
Target entity description: MLton is a whole-program optimizing compiler for the Standard ML programming language, known for its aggressive optimizations and high-performance native code generation.
  • A. MLT
    MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
  • B. Talbo
    Talbo is the surname of Dolly Talbo, a character whose last name identifies her within her fictional or narrative family lineage.
  • C. Maltoni
    Maltoni is the Italian maiden surname of Rosa Maltoni Mussolini, the mother of fascist dictator Benito Mussolini.
  • D. Mattertal
    Mattertal is a high alpine valley in the Swiss canton of Valais, known for its dramatic peaks including the Matterhorn and its popular mountaineering and ski resorts.
  • E. Molten
    Molten is a Japanese sports equipment manufacturer best known for producing high-quality balls used in major international competitions across football, basketball, and other sports.
  • 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_69ca847f22188190a56e4a97625bef22 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd996df4f08190b19bbaefb10a9789 completed April 1, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69d152b09c808190aff32419f2cbb15f completed April 4, 2026, 6:04 p.m.
NEDg Description generation batch_69d153d59844819086a0f50e6a7624b2 completed April 4, 2026, 6:09 p.m.
NED2 Entity disambiguation (via description) batch_69d1546a503c81908edc9588adabc172 completed April 4, 2026, 6:11 p.m.
Created at: March 30, 2026, 8:04 p.m.