Triple

T1238514
Position Surface form Disambiguated ID Type / Status
Subject Réunion E26602 entity
Predicate ISO3166-1Alpha2Code P189 FINISHED
Object RE
RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
 E142638 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: RE | Statement: [Réunion, ISO3166-1Alpha2Code, RE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RE
Context triple: [Réunion, ISO3166-1Alpha2Code, RE]
  • A. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • B. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • C. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • D. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • E. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • 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: RE
Triple: [Réunion, ISO3166-1Alpha2Code, RE]
Generated description
RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RE
Target entity description: RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
  • A. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • B. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • C. ER
    ER is the commonly used abbreviation for United Russia, the dominant ruling political party in the Russian Federation.
  • D. ER
    ER is a critically acclaimed American medical drama television series that follows the personal and professional lives of staff in a busy Chicago emergency room.
  • E. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • 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_69a4948689d08190b3a4a3f388c02148 completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4bf406b988190a12aa26bbcb88d6a completed March 1, 2026, 10:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac8f7755fc819089e23eca81583885 completed March 7, 2026, 8:49 p.m.
NEDg Description generation batch_69ac903eb12c8190a4024a71f15b19a4 completed March 7, 2026, 8:53 p.m.
NED2 Entity disambiguation (via description) batch_69ac916522b081908401b89261f99d50 completed March 7, 2026, 8:58 p.m.
Created at: March 1, 2026, 7:47 p.m.