Triple

T17779739
Position Surface form Disambiguated ID Type / Status
Subject Documenta (Kassel) E443863 entity
Predicate hashtag P9354 FINISHED
Object #documenta
#documenta is the commonly used social media hashtag for the Documenta contemporary art exhibition held in Kassel, Germany.
 E1287906 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: #documenta | Statement: [Documenta (Kassel), hashtag, #documenta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: #documenta
Context triple: [Documenta (Kassel), hashtag, #documenta]
  • A. DOCO
    DOCO is a mixed-use entertainment, shopping, and dining district in downtown Sacramento, California, adjacent to the Golden 1 Center.
  • B. DOCU
    DOCU is the stock ticker symbol for DocuSign, a leading provider of electronic signature and digital agreement management solutions.
  • C. Docus
    Docus is an alternative name for Dok, likely referring to the same entity, brand, or product under a different designation.
  • D. DOC
    DOC is the commonly used abbreviation for the New York City Department of Correction, the agency responsible for operating the city’s jail system.
  • E. DOC
    DOC is the commonly used abbreviation for the Division of Organic Chemistry, a professional organization focused on advancing research and education in organic chemistry.
  • 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: #documenta
Triple: [Documenta (Kassel), hashtag, #documenta]
Generated description
#documenta is the commonly used social media hashtag for the Documenta contemporary art exhibition held in Kassel, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: #documenta
Target entity description: #documenta is the commonly used social media hashtag for the Documenta contemporary art exhibition held in Kassel, Germany.
  • A. DOCO
    DOCO is a mixed-use entertainment, shopping, and dining district in downtown Sacramento, California, adjacent to the Golden 1 Center.
  • B. DOCU
    DOCU is the stock ticker symbol for DocuSign, a leading provider of electronic signature and digital agreement management solutions.
  • C. Docus
    Docus is an alternative name for Dok, likely referring to the same entity, brand, or product under a different designation.
  • D. DOC
    DOC is the commonly used abbreviation for the New York City Department of Correction, the agency responsible for operating the city’s jail system.
  • E. DOC
    DOC is the commonly used abbreviation for the Division of Organic Chemistry, a professional organization focused on advancing research and education in organic chemistry.
  • 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_69d8b9ef17708190bdf7e2adbf14ddc2 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48720734c819085ebe37065b8c8f3 completed April 19, 2026, 7:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02f8321e288190be4228b6975f372d completed May 12, 2026, 9:51 a.m.
NEDg Description generation batch_6a02f94b4748819096a529f04525cf03 completed May 12, 2026, 9:56 a.m.
NED2 Entity disambiguation (via description) batch_6a02fa28ac0881909d9d4273ea576273 completed May 12, 2026, 10 a.m.
Created at: April 10, 2026, 10:12 a.m.