Triple

T24651930
Position Surface form Disambiguated ID Type / Status
Subject ISO 3166-2:HT E610271 entity
Predicate subdivisionNameExample P157663 FINISHED
Object Nord-Est
Nord-Est is a department in northeastern Haiti known for its border with the Dominican Republic and coastal location along the Atlantic Ocean.
 E1645766 NE FINISHED

How this triple was built (3 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: Nord-Est | Statement: [ISO 3166-2:HT, subdivisionNameExample, Nord-Est]
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: Nord-Est
Triple: [ISO 3166-2:HT, subdivisionNameExample, Nord-Est]
Generated description
Nord-Est is a department in northeastern Haiti known for its border with the Dominican Republic and coastal location along the Atlantic Ocean.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: subdivisionNameExample
Context triple: [ISO 3166-2:HT, subdivisionNameExample, Nord-Est]
  • A. subdivisionNameExample chosen
    Indicates that the predicate provides an example name of a subdivision (such as a district, region, or administrative area) associated with an entity.
  • B. subdivisionNameType
    Indicates the type or category of a named geographic or administrative subdivision (e.g., province, state, district) associated with an entity.
  • C. subdivisionName0
    Indicates the name assigned to the first (primary) subdivision or sub-unit associated with an entity.
  • D. subdivisionName1
    Indicates that the first named subdivision is identified by a specific name or designation within a larger geographic or organizational hierarchy.
  • E. subdivisionNameLocal
    Indicates the locally used or native-language name assigned to a specific administrative or geographic subdivision.
  • F. None of above.

Provenance (6 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_69e2c4d350a481909170482bc2ce6af9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f464b4c9b0819085daa00c7c3b8b76 completed May 1, 2026, 8:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10049ac3a481909fc8eae6b8f9cdf8 completed May 22, 2026, 7:24 a.m.
NEDg Description generation batch_6a10095d986881909082cc5a32b6d56e completed May 22, 2026, 7:44 a.m.
NED2 Entity disambiguation (via description) batch_6a1009d311b08190acf35a3ed552b9c1 completed May 22, 2026, 7:46 a.m.
PD Predicate disambiguation batch_69f45cf017a88190b4985b11159c907d completed May 1, 2026, 7:57 a.m.
Created at: April 18, 2026, 2:34 a.m.