Triple

T1960951
Position Surface form Disambiguated ID Type / Status
Subject Yale Graduate School of Arts and Sciences E42384 entity
Predicate hasDegree P6482 FINISHED
Object MS
MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
 E219365 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: MS | Statement: [Yale Graduate School of Arts and Sciences, hasDegree, MS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MS
Context triple: [Yale Graduate School of Arts and Sciences, hasDegree, MS]
  • A. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • B. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • C. MS
    MS is the official vehicle registration code used on license plates for the German city of Münster.
  • D. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • E. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • 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: MS
Triple: [Yale Graduate School of Arts and Sciences, hasDegree, MS]
Generated description
MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MS
Target entity description: MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
  • A. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • B. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • C. MS
    MS is the official vehicle registration code used on license plates for the German city of Münster.
  • D. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • E. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • 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_69a8870eea088190a38781990812a9bc completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb380bfc08190ae80f8e6570494b8 completed March 7, 2026, 5:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfbcea048819091d705095f0d3f68 completed March 8, 2026, 10:44 p.m.
NEDg Description generation batch_69adfc8efb0c81908bce5a4a13359801 completed March 8, 2026, 10:47 p.m.
NED2 Entity disambiguation (via description) batch_69adfd8115d481909716e11b943cbf61 completed March 8, 2026, 10:51 p.m.
Created at: March 4, 2026, 7:36 p.m.