Triple

T12899920
Position Surface form Disambiguated ID Type / Status
Subject pgfplots E308585 entity
Predicate integratesWith P1075 FINISHED
Object Beamer
Beamer is a LaTeX class widely used for creating professional presentation slides, especially in academic and scientific contexts.
 E1008104 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: Beamer | Statement: [pgfplots, integratesWith, Beamer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Beamer
Context triple: [pgfplots, integratesWith, Beamer]
  • A. Bortus
    Bortus is a stoic, duty-bound Moclan officer serving as second-in-command aboard the exploratory spaceship in the sci-fi comedy series "The Orville."
  • B. Tantor
    Tantor is the timid yet loyal elephant character from Disney's Tarzan franchise.
  • C. Spektr
    Spektr was a Russian-built science module of the Mir space station, primarily used for Earth observation and housing U.S. research equipment during the Shuttle–Mir program.
  • D. Recetor
    Recetor is a small rural municipality located in the Casanare Department of eastern Colombia, known for its agricultural and livestock-based economy.
  • E. Boe
    Boe is a city in Guinea-Bissau known as the site where the Boé Declaration on Regional Security was adopted.
  • 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: Beamer
Triple: [pgfplots, integratesWith, Beamer]
Generated description
Beamer is a LaTeX class widely used for creating professional presentation slides, especially in academic and scientific contexts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Beamer
Target entity description: Beamer is a LaTeX class widely used for creating professional presentation slides, especially in academic and scientific contexts.
  • A. Bortus
    Bortus is a stoic, duty-bound Moclan officer serving as second-in-command aboard the exploratory spaceship in the sci-fi comedy series "The Orville."
  • B. Tantor
    Tantor is the timid yet loyal elephant character from Disney's Tarzan franchise.
  • C. Spektr
    Spektr was a Russian-built science module of the Mir space station, primarily used for Earth observation and housing U.S. research equipment during the Shuttle–Mir program.
  • D. Recetor
    Recetor is a small rural municipality located in the Casanare Department of eastern Colombia, known for its agricultural and livestock-based economy.
  • E. Boe
    Boe is a city in Guinea-Bissau known as the site where the Boé Declaration on Regional Security was adopted.
  • 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_69d7bdf7c1f0819098102569a8d8cbf5 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97180ee708190b60a3e58c42f764f completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6a56189b081909ed838addcb6d265 completed May 3, 2026, 1:31 a.m.
NEDg Description generation batch_69f6a6179cdc8190976daa1384032445 completed May 3, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_69f6a6cbec348190a96a0194b2d6be4b completed May 3, 2026, 1:37 a.m.
Created at: April 9, 2026, 5:40 p.m.