Triple

T27318274
Position Surface form Disambiguated ID Type / Status
Subject Rheingönheim E689408 entity
Predicate hasLandmark P105 FINISHED
Object Protestant church of Rheingönheim
The Protestant church of Rheingönheim is a local Christian place of worship serving the Protestant community in the Rheingönheim district of Ludwigshafen am Rhein, Germany.
E1765943 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: Protestant church of Rheingönheim | Statement: [Rheingönheim, hasLandmark, Protestant church of Rheingönheim]
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: Protestant church of Rheingönheim
Triple: [Rheingönheim, hasLandmark, Protestant church of Rheingönheim]
Generated description
The Protestant church of Rheingönheim is a local Christian place of worship serving the Protestant community in the Rheingönheim district of Ludwigshafen am Rhein, Germany.

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_69ef355c53a08190a8a92e355a7ce115 completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f627e861e08190944ede3f79518a0d completed May 2, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129cb8941481909bd229aa81a72fce completed May 24, 2026, 6:37 a.m.
NEDg Description generation batch_6a129d7477bc8190ac1956dc68c4df75 completed May 24, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_6a129e4151208190995590e78cf35502 completed May 24, 2026, 6:44 a.m.
Created at: April 27, 2026, 11:31 a.m.