Triple

T19346768
Position Surface form Disambiguated ID Type / Status
Subject Tihomir Orešković E483898 entity
Predicate employer P7 FINISHED
Object Pliva
Pliva is a Croatian pharmaceutical company known for developing the antibiotic azithromycin and being one of the largest drug manufacturers in Central and Eastern Europe.
 E1370959 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: Pliva | Statement: [Tihomir Orešković, employer, Pliva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pliva
Context triple: [Tihomir Orešković, employer, Pliva]
  • A. Pliva
    Pliva is a river in western Bosnia and Herzegovina known for its scenic waterfalls and lakes near the town of Jajce.
  • B. Sandoz
    Sandoz is a historic Swiss pharmaceutical company best known as a predecessor of Novartis and a major player in generic medicines.
  • C. Roche
    Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
  • D. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • E. Bayer
    Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
  • 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: Pliva
Triple: [Tihomir Orešković, employer, Pliva]
Generated description
Pliva is a Croatian pharmaceutical company known for developing the antibiotic azithromycin and being one of the largest drug manufacturers in Central and Eastern Europe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pliva
Target entity description: Pliva is a Croatian pharmaceutical company known for developing the antibiotic azithromycin and being one of the largest drug manufacturers in Central and Eastern Europe.
  • A. Pliva
    Pliva is a river in western Bosnia and Herzegovina known for its scenic waterfalls and lakes near the town of Jajce.
  • B. Sandoz
    Sandoz is a historic Swiss pharmaceutical company best known as a predecessor of Novartis and a major player in generic medicines.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Roche
    Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
  • E. Bayer
    Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
  • 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_69d8e8d244f8819080eb1f3491300db2 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e6185b7d348190ba195056bb32c765 completed April 20, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a071bed1c608190a71094aa2645199d completed May 15, 2026, 1:13 p.m.
NEDg Description generation batch_6a071cc1c76c8190b99ef2b319f6ab39 completed May 15, 2026, 1:16 p.m.
NED2 Entity disambiguation (via description) batch_6a071d787dd881908d86c5fe52ca2561 completed May 15, 2026, 1:19 p.m.
Created at: April 10, 2026, 1:34 p.m.