Triple

T2963057
Position Surface form Disambiguated ID Type / Status
Subject Erlangen E80092 entity
Predicate hasDistrict P459 FINISHED
Object Tennenlohe
Tennenlohe is a district of Erlangen in Bavaria, Germany, known for its proximity to research institutions and the Tennenlohe Forest nature reserve.
 E316840 NE FINISHED

How this triple was built (4 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: Tennenlohe | Statement: [Erlangen, hasDistrict, Tennenlohe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tennenlohe
Context triple: [Erlangen, hasDistrict, Tennenlohe]
  • A. Nadelhorn
    Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
  • B. Mount Nivea
    Mount Nivea is a prominent mountain peak that forms the highest point in the remote South Orkney Islands of the Southern Ocean.
  • C. Wilseder Berg
    Wilseder Berg is a prominent hill and popular viewpoint in northern Germany, known for its scenic heathland landscapes within the Lüneburg Heath region.
  • D. Brocken
    Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
  • E. Hoche
    Hoche is a Paris Métro station located in the northeastern suburb of Pantin, serving as a stop on the city’s Line 5.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Tennenlohe
Triple: [Erlangen, hasDistrict, Tennenlohe]
Generated description
Tennenlohe is a district of Erlangen in Bavaria, Germany, known for its proximity to research institutions and the Tennenlohe Forest nature reserve.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tennenlohe
Target entity description: Tennenlohe is a district of Erlangen in Bavaria, Germany, known for its proximity to research institutions and the Tennenlohe Forest nature reserve.
  • A. Nadelhorn
    Nadelhorn is a prominent 4,000-meter-class peak in the Swiss Alps, known for its sharp, needle-like summit and popular alpine climbing routes.
  • B. Mount Nivea
    Mount Nivea is a prominent mountain peak that forms the highest point in the remote South Orkney Islands of the Southern Ocean.
  • C. Wilseder Berg
    Wilseder Berg is a prominent hill and popular viewpoint in northern Germany, known for its scenic heathland landscapes within the Lüneburg Heath region.
  • D. Brocken
    Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
  • E. Hoche
    Hoche is a Paris Métro station located in the northeastern suburb of Pantin, serving as a stop on the city’s Line 5.
  • F. None of above. chosen

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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9957602c819089b673966fd619e0 completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108e14e288190bcca59b2d8132996 completed March 11, 2026, 6:17 a.m.
NEDg Description generation batch_69b10e65ae8c81908f4f9ba9dd4de206 completed March 11, 2026, 6:40 a.m.
NED2 Entity disambiguation (via description) batch_69b10ece90e88190b84dde41579bf751 completed March 11, 2026, 6:42 a.m.
Created at: March 8, 2026, 2:57 p.m.