Triple

T4880813
Position Surface form Disambiguated ID Type / Status
Subject Nina Agdal E109320 entity
Predicate hasWorkedFor P11675 FINISHED
Object Lysse
Lysse is a fashion and apparel brand known for its stylish, comfortable leggings and women’s clothing.
 E477103 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: Lysse | Statement: [Nina Agdal, hasWorkedFor, Lysse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lysse
Context triple: [Nina Agdal, hasWorkedFor, Lysse]
  • A. Lisea
    Lisea is a French private consortium that finances, builds, and operates the high-speed rail line LGV Sud Europe Atlantique between Tours and Bordeaux.
  • B. Lyss
    Lyss is a Swiss municipality in the canton of Bern, known as a regional transport hub and residential town within the greater Bern area.
  • C. Lys
    Lys is a wealthy and decadent island city-state in the world of *A Song of Ice and Fire* known for its pleasure houses, skilled courtesans, and distinctive Valyrian-descended population.
  • D. Lys
    Lys is a utopian, technologically advanced city in Arthur C. Clarke’s early science fiction universe, known for its preserved vitality and contrast to Earth’s stagnation.
  • E. Lys
    The Lys is a river in northern France and western Belgium that flows through cities like Ghent and is known for its historical role in trade and the textile industry.
  • 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: Lysse
Triple: [Nina Agdal, hasWorkedFor, Lysse]
Generated description
Lysse is a fashion and apparel brand known for its stylish, comfortable leggings and women’s clothing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lysse
Target entity description: Lysse is a fashion and apparel brand known for its stylish, comfortable leggings and women’s clothing.
  • A. Lisea
    Lisea is a French private consortium that finances, builds, and operates the high-speed rail line LGV Sud Europe Atlantique between Tours and Bordeaux.
  • B. Lyss
    Lyss is a Swiss municipality in the canton of Bern, known as a regional transport hub and residential town within the greater Bern area.
  • C. Lys
    Lys is a wealthy and decadent island city-state in the world of *A Song of Ice and Fire* known for its pleasure houses, skilled courtesans, and distinctive Valyrian-descended population.
  • D. Lys
    The Lys is a river in northern France and western Belgium that flows through cities like Ghent and is known for its historical role in trade and the textile industry.
  • E. Lys
    Lys is a utopian, technologically advanced city in Arthur C. Clarke’s early science fiction universe, known for its preserved vitality and contrast to Earth’s stagnation.
  • 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_69bd440e9d64819083e82cf33b4d9570 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6dc071d4819083ea9fd0c73c5f49 completed March 20, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6803a1c081908972984241276c19 completed March 21, 2026, 9:42 a.m.
NEDg Description generation batch_69be6aadda048190a5b9276f59080b3e completed March 21, 2026, 9:53 a.m.
NED2 Entity disambiguation (via description) batch_69be6b0e7ca08190861992a251b3dbd9 completed March 21, 2026, 9:55 a.m.
Created at: March 20, 2026, 1:27 p.m.