Triple

T31350255
Position Surface form Disambiguated ID Type / Status
Subject Moritzburg E799568 entity
Predicate hasLandmark P105 FINISHED
Object Moritzburg lighthouse
Moritzburg lighthouse is a small, historic mock lighthouse built in the 18th century as part of a baroque maritime-themed landscape near Moritzburg Castle in Saxony, Germany.
E1959791 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: Moritzburg lighthouse | Statement: [Moritzburg, hasLandmark, Moritzburg lighthouse]
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: Moritzburg lighthouse
Triple: [Moritzburg, hasLandmark, Moritzburg lighthouse]
Generated description
Moritzburg lighthouse is a small, historic mock lighthouse built in the 18th century as part of a baroque maritime-themed landscape near Moritzburg Castle in Saxony, 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_69f224e51614819083141459a080e97c completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69f40ae2481909321485a3f63a3e4 completed May 3, 2026, 1:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad22e9c648190bed1d07098e9d2a1 completed June 11, 2026, 3:20 p.m.
NEDg Description generation batch_6a2ad33afe208190b4ca10449a606dc2 completed June 11, 2026, 3:24 p.m.
NED2 Entity disambiguation (via description) batch_6a2add6a1e1c819091172fd1997fac30 completed June 11, 2026, 4:08 p.m.
Created at: April 29, 2026, 9:17 p.m.