Triple

T10990609
Position Surface form Disambiguated ID Type / Status
Subject Group of Experts on Action against Trafficking in Human Beings E259744 entity
Predicate shortName P43 FINISHED
Object GRETA
GRETA is an expert monitoring body of the Council of Europe that evaluates how member states implement measures to prevent and combat human trafficking and protect its victims.
 E898450 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: GRETA | Statement: [Group of Experts on Action against Trafficking in Human Beings, shortName, GRETA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GRETA
Context triple: [Group of Experts on Action against Trafficking in Human Beings, shortName, GRETA]
  • A. Greta
    Greta is a feminine given name, commonly used as a diminutive or variant of names like Margaret in various European languages.
  • B. Greta
    Greta is a small town located within the Hunter Region of New South Wales, Australia.
  • C. GERDA
    GERDA is a physics experiment at Italy’s Gran Sasso underground laboratory designed to search for neutrinoless double beta decay in germanium-76.
  • D. Gert
    Gert is a given name, commonly used as a diminutive form of Gerard in various European countries.
  • E. Greeta
    Greeta is a small tributary stream in northern England that feeds into the River Wenning.
  • 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: GRETA
Triple: [Group of Experts on Action against Trafficking in Human Beings, shortName, GRETA]
Generated description
GRETA is an expert monitoring body of the Council of Europe that evaluates how member states implement measures to prevent and combat human trafficking and protect its victims.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GRETA
Target entity description: GRETA is an expert monitoring body of the Council of Europe that evaluates how member states implement measures to prevent and combat human trafficking and protect its victims.
  • A. Greta
    Greta is a small town located within the Hunter Region of New South Wales, Australia.
  • B. Greta
    Greta is a feminine given name, commonly used as a diminutive or variant of names like Margaret in various European languages.
  • C. GERDA
    GERDA is a physics experiment at Italy’s Gran Sasso underground laboratory designed to search for neutrinoless double beta decay in germanium-76.
  • D. Gert
    Gert is a given name, commonly used as a diminutive form of Gerard in various European countries.
  • E. Greeta
    Greeta is a small tributary stream in northern England that feeds into the River Wenning.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d787b8d7088190b7d6b63c3bac4ad1 completed April 9, 2026, 11:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69e34504ebec8190a78e4795765b0c24 completed April 18, 2026, 8:47 a.m.
NEDg Description generation batch_69e3556e8b408190a02a1fe194ae5750 completed April 18, 2026, 9:57 a.m.
NED2 Entity disambiguation (via description) batch_69e3593b0f8481909ed7a90f8bb9839d completed April 18, 2026, 10:13 a.m.
Created at: April 8, 2026, 9:24 p.m.