Triple

T33365780
Position Surface form Disambiguated ID Type / Status
Subject Eugen Popescu Stadium E854346 entity
Predicate hasHomeTeam P346 FINISHED
Object Chindia Târgoviște
Chindia Târgoviște is a Romanian professional football club based in the city of Târgoviște.
E2053135 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: Chindia Târgoviște | Statement: [Eugen Popescu Stadium, hasHomeTeam, Chindia Târgoviște]
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: Chindia Târgoviște
Triple: [Eugen Popescu Stadium, hasHomeTeam, Chindia Târgoviște]
Generated description
Chindia Târgoviște is a Romanian professional football club based in the city of Târgoviște.

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_69f3496bda8c8190bfc8fade9d1b791c completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6dfd118948190b46b1bd1c8fd1cc8 completed May 3, 2026, 5:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3595968d9c8190adb4f10f44e0869b completed June 19, 2026, 7:16 p.m.
NEDg Description generation batch_6a35966fe39481908df28167e7d613ad completed June 19, 2026, 7:20 p.m.
NED2 Entity disambiguation (via description) batch_6a3597a9451c8190ae497a25ac6513be completed June 19, 2026, 7:25 p.m.
Created at: May 1, 2026, 1:35 a.m.