Triple

T34491185
Position Surface form Disambiguated ID Type / Status
Subject Ovan E885469 entity
Predicate geographicRegionOfCountry P8031 FINISHED
Object northeastern Gabon
Northeastern Gabon is a sparsely populated, forested region of Gabon known for its rich biodiversity, river systems, and proximity to several national parks.
 E2098794 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: northeastern Gabon | Statement: [Ovan, geographicRegionOfCountry, northeastern Gabon]
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: northeastern Gabon
Triple: [Ovan, geographicRegionOfCountry, northeastern Gabon]
Generated description
Northeastern Gabon is a sparsely populated, forested region of Gabon known for its rich biodiversity, river systems, and proximity to several national parks.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: geographicRegionOfCountry
Context triple: [Ovan, geographicRegionOfCountry, northeastern Gabon]
  • A. geographicalRegionType
    Indicates the specific kind or category of geographical region that an entity belongs to (e.g., continent, country, province, or city).
  • B. countryRegion chosen
    Indicates that a country is located within, or belongs to, a specific geographic or administrative region.
  • C. ISORegion
    Indicates a standardized geographic or administrative region as defined by an ISO (International Organization for Standardization) code.
  • D. longitudeRegion
    Indicates that an entity is located within or associated with a specific longitudinal region on the Earth’s surface.
  • E. countryOrRegion
    Indicates that one entity is a country or geographic region associated with another entity (such as its location, jurisdiction, or area of relevance).
  • 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_69f349cafcec8190997b45b3fdc16c27 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69fbaebc8f2c8190b94f1b4a3ec92e8c completed May 6, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37213fc5ac819087b0d209e5081a65 completed June 20, 2026, 11:24 p.m.
NEDg Description generation batch_6a37221583e48190a3dbe0dc7ad27453 completed June 20, 2026, 11:28 p.m.
NED2 Entity disambiguation (via description) batch_6a3722b425808190a8453a3e71d14066 completed June 20, 2026, 11:31 p.m.
PD Predicate disambiguation batch_69fbadf1e6008190a71bbd196ba06844 completed May 6, 2026, 9:09 p.m.
Created at: May 1, 2026, 2:01 a.m.