Triple

T30267033
Position Surface form Disambiguated ID Type / Status
Subject The Chocolate Soldier E769676 entity
Predicate hasEnglishLibrettoAdapter P60682 FINISHED
Object Stanford Daly
Stanford Daly was an English librettist best known for adapting the libretto of the operetta "The Chocolate Soldier" for English-speaking audiences.
 E1916268 NE FINISHED

How this triple was built (3 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: Stanford Daly | Statement: [The Chocolate Soldier, hasEnglishLibrettoAdapter, Stanford Daly]
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: Stanford Daly
Triple: [The Chocolate Soldier, hasEnglishLibrettoAdapter, Stanford Daly]
Generated description
Stanford Daly was an English librettist best known for adapting the libretto of the operetta "The Chocolate Soldier" for English-speaking audiences.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasEnglishLibrettoAdapter
Context triple: [The Chocolate Soldier, hasEnglishLibrettoAdapter, Stanford Daly]
  • A. hasEnglishLyricAdapter chosen
    Indicates that an entity has a specific person or agent responsible for adapting its lyrics into English.
  • B. originalLanguageOfLibretto
    Indicates the language in which a libretto was originally written for a given work.
  • C. includesLibretto
    Indicates that one entity (typically a musical or operatic work or publication) contains or is accompanied by the full text/libretto of another work.
  • D. librettoAdaptationToLanguage
    Indicates that a libretto has been adapted or translated into a specific target language.
  • E. hasLibrettoPublication
    Indicates that a work is associated with a specific published version of its libretto.
  • F. None of above.

Provenance (6 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_69f224856d9881908c7f0dd64f059672 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f7a225a77c81908f8953ccfeb14336 completed May 3, 2026, 7:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27989b92288190b554cd99151acb69 completed June 9, 2026, 4:37 a.m.
NEDg Description generation batch_6a27a6d8685c819088e8900160bbfe73 completed June 9, 2026, 5:38 a.m.
NED2 Entity disambiguation (via description) batch_6a27a76a4ce0819081de47aefcca8d5a completed June 9, 2026, 5:40 a.m.
PD Predicate disambiguation batch_69f7a06d4f108190bae3ab9ae431d2c7 completed May 3, 2026, 7:22 p.m.
Created at: April 29, 2026, 7:43 p.m.