Triple

T616880
Position Surface form Disambiguated ID Type / Status
Subject Quakenbrück E14423 entity
Predicate locatedOnRiver P165 FINISHED
Object Hase
The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
 E77450 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: Hase | Statement: [Quakenbrück, locatedOnRiver, Hase]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hase
Context triple: [Quakenbrück, locatedOnRiver, Hase]
  • A. Hama
    Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
  • B. Havah
    Havah is a transliteration of the Hebrew name for Eve, the first woman in the biblical creation narrative.
  • C. Haya
    Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
  • D. Harauti
    Harauti is an Indo-Aryan dialect of the Rajasthani language spoken primarily in the Hadoti region of Rajasthan, India.
  • E. Habikino
    Habikino is a city in Osaka Prefecture, Japan, known for its historic kofun burial mounds and role within the Osaka metropolitan area.
  • 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: Hase
Triple: [Quakenbrück, locatedOnRiver, Hase]
Generated description
The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hase
Target entity description: The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
  • A. Hama
    Hama is a major city in west-central Syria, historically known for its ancient waterwheels (norias) on the Orontes River and its role as an important agricultural and industrial center.
  • B. Havah
    Havah is a transliteration of the Hebrew name for Eve, the first woman in the biblical creation narrative.
  • C. Haya
    Haya is a feminine given name of Arabic origin, commonly used in the Middle East and among Arabic-speaking communities.
  • D. Harauti
    Harauti is an Indo-Aryan dialect of the Rajasthani language spoken primarily in the Hadoti region of Rajasthan, India.
  • E. Habikino
    Habikino is a city in Osaka Prefecture, Japan, known for its historic kofun burial mounds and role within the Osaka metropolitan area.
  • 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_69a4934b17c881909ace8270e8ddd202 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49e22f3688190a512bec3f0347814 completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a55a77b6648190a2d07471442b401a completed March 2, 2026, 9:37 a.m.
NEDg Description generation batch_69a55b80320c8190a4e9eba92cd2839a completed March 2, 2026, 9:42 a.m.
NED2 Entity disambiguation (via description) batch_69a55bcfce508190b58e0289775125f9 completed March 2, 2026, 9:43 a.m.
Created at: March 1, 2026, 7:35 p.m.