Triple

T3928673
Position Surface form Disambiguated ID Type / Status
Subject Main Tower E93339 entity
Predicate owner P347 FINISHED
Object Helaba
Helaba is a major German public-sector commercial bank, formally known as Landesbank Hessen-Thüringen, that provides wholesale, retail, and public finance services.
 E399863 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: Helaba | Statement: [Main Tower, owner, Helaba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Helaba
Context triple: [Main Tower, owner, Helaba]
  • A. Labayu
    Labayu was a 14th-century BCE Canaanite ruler known from the Amarna letters for his aggressive expansionism and conflicts with neighboring city-states.
  • B. Ilo
    Ilo is a given name associated with the individual Ilo Browne Wallace.
  • C. Moura
    Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
  • D. Cieneguilla
    Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
  • E. Étex
    Étex is a French surname most notably borne by Antoine Étex, a 19th-century sculptor, painter, and architect.
  • 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: Helaba
Triple: [Main Tower, owner, Helaba]
Generated description
Helaba is a major German public-sector commercial bank, formally known as Landesbank Hessen-Thüringen, that provides wholesale, retail, and public finance services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Helaba
Target entity description: Helaba is a major German public-sector commercial bank, formally known as Landesbank Hessen-Thüringen, that provides wholesale, retail, and public finance services.
  • A. Labayu
    Labayu was a 14th-century BCE Canaanite ruler known from the Amarna letters for his aggressive expansionism and conflicts with neighboring city-states.
  • B. Ilo
    Ilo is a given name associated with the individual Ilo Browne Wallace.
  • C. Moura
    Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
  • D. Cieneguilla
    Cieneguilla is a semi-rural district in the eastern part of Lima, Peru, known for its natural landscapes, country houses, and outdoor recreation areas.
  • E. Étex
    Étex is a French surname most notably borne by Antoine Étex, a 19th-century sculptor, painter, and architect.
  • 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_69aed96bfa1081908f7b30f2c647dee6 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeeda65b708190b24cd715915aec1d completed March 9, 2026, 3:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5287b8d548190a929f14637cb9963 completed March 14, 2026, 9:20 a.m.
NEDg Description generation batch_69b529ad13d48190995ed79d3c41a69b completed March 14, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_69b52a5274ec8190b481a2627e94addb completed March 14, 2026, 9:28 a.m.
Created at: March 9, 2026, 3:23 p.m.