Triple

T27060126
Position Surface form Disambiguated ID Type / Status
Subject Savski venac E685018 entity
Predicate contains P35 FINISHED
Object St. Sava Hospital and medical facilities cluster
St. Sava Hospital and medical facilities cluster is a major healthcare complex in Belgrade that brings together multiple medical institutions and services in one area.
E1752920 NE FINISHED

How this triple was built (2 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: St. Sava Hospital and medical facilities cluster | Statement: [Savski venac, contains, St. Sava Hospital and medical facilities cluster]
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: St. Sava Hospital and medical facilities cluster
Triple: [Savski venac, contains, St. Sava Hospital and medical facilities cluster]
Generated description
St. Sava Hospital and medical facilities cluster is a major healthcare complex in Belgrade that brings together multiple medical institutions and services in one area.

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_69ef14835fcc81908bd737b4267ae528 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f622e3ab7081909692e4857e7d7633 completed May 2, 2026, 4:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123acea7208190978f664e8000af6c completed May 23, 2026, 11:39 p.m.
NEDg Description generation batch_6a123bd93ac081909b060b395b1d3e81 completed May 23, 2026, 11:44 p.m.
NED2 Entity disambiguation (via description) batch_6a123c56268c81909d0e71dad0aeab01 completed May 23, 2026, 11:46 p.m.
Created at: April 27, 2026, 8:20 a.m.