Triple

T24136869
Position Surface form Disambiguated ID Type / Status
Subject Stari Grad, Belgrade E598111 entity
Predicate contains P35 FINISHED
Object Students’ Park, Belgrade
Students’ Park in Belgrade is a historic central city park and popular gathering place for students and locals, known for its monuments, greenery, and proximity to major educational and cultural institutions.
E1625263 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: Students’ Park, Belgrade | Statement: [Stari Grad, Belgrade, contains, Students’ Park, Belgrade]
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: Students’ Park, Belgrade
Triple: [Stari Grad, Belgrade, contains, Students’ Park, Belgrade]
Generated description
Students’ Park in Belgrade is a historic central city park and popular gathering place for students and locals, known for its monuments, greenery, and proximity to major educational and cultural institutions.

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_69e288c92e448190ac57034fa0c863ce completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1df7d30a0819092ecea0cf58c2332 completed April 29, 2026, 10:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fbd0463148190ba03f12db685125b completed May 22, 2026, 2:18 a.m.
NEDg Description generation batch_6a0fc03e594c8190aad5eed4e6006ffc completed May 22, 2026, 2:32 a.m.
NED2 Entity disambiguation (via description) batch_6a0fc15d85ec8190841005d247ad01f3 completed May 22, 2026, 2:37 a.m.
Created at: April 17, 2026, 11:27 p.m.