Triple

T3917428
Position Surface form Disambiguated ID Type / Status
Subject Enz E88874 entity
Predicate hasLeftTributary P415 FINISHED
Object Metter
The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
 E400065 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: Metter | Statement: [Enz, hasLeftTributary, Metter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Metter
Context triple: [Enz, hasLeftTributary, Metter]
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • B. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • C. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • D. Mechta
    Mechta is the alternative name for Luna 1, the Soviet spacecraft that became the first human-made object to reach the vicinity of the Moon and enter a heliocentric orbit.
  • E. Melle
    Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
  • 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: Metter
Triple: [Enz, hasLeftTributary, Metter]
Generated description
The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Metter
Target entity description: The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • B. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • C. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • D. Mechta
    Mechta is the alternative name for Luna 1, the Soviet spacecraft that became the first human-made object to reach the vicinity of the Moon and enter a heliocentric orbit.
  • E. Melle
    Melle is a town in Lower Saxony, Germany, known for its rural character, historical architecture, and role as a regional economic center.
  • 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_69aed955229881909e85e73ffab1d343 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeed5797508190adaddb84575d9bb3 completed March 9, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b52864e0488190ab348a52cb9168b9 completed March 14, 2026, 9:20 a.m.
NEDg Description generation batch_69b52c3423988190aae6514041dc8ab3 completed March 14, 2026, 9:36 a.m.
NED2 Entity disambiguation (via description) batch_69b52ca9443481909e43a8bbe208c807 completed March 14, 2026, 9:38 a.m.
Created at: March 9, 2026, 3:22 p.m.