Triple

T6243916
Position Surface form Disambiguated ID Type / Status
Subject Nggela (Florida) Islands E139668 entity
Predicate hasIsland P970 FINISHED
Object Tanambogo
Tanambogo is a small island in the Central Province of the Solomon Islands, notable for its role as a Japanese seaplane base and site of intense fighting during World War II.
 E578969 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: Tanambogo | Statement: [Nggela (Florida) Islands, hasIsland, Tanambogo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tanambogo
Context triple: [Nggela (Florida) Islands, hasIsland, Tanambogo]
  • A. Abamakoro
    Abamakoro is a small village located on the island of Nonouti in the Republic of Kiribati in the central Pacific Ocean.
  • B. Bitonga
    Bitonga is a Bantu language spoken primarily by the Bitonga people in Mozambique’s Inhambane Province.
  • C. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • D. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • E. Tallimba
    Tallimba is a small rural locality in the Riverina region of New South Wales, Australia, situated within an agricultural area west of the town of West Wyalong.
  • 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: Tanambogo
Triple: [Nggela (Florida) Islands, hasIsland, Tanambogo]
Generated description
Tanambogo is a small island in the Central Province of the Solomon Islands, notable for its role as a Japanese seaplane base and site of intense fighting during World War II.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tanambogo
Target entity description: Tanambogo is a small island in the Central Province of the Solomon Islands, notable for its role as a Japanese seaplane base and site of intense fighting during World War II.
  • A. Abamakoro
    Abamakoro is a small village located on the island of Nonouti in the Republic of Kiribati in the central Pacific Ocean.
  • B. Bitonga
    Bitonga is a Bantu language spoken primarily by the Bitonga people in Mozambique’s Inhambane Province.
  • C. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • D. Kibondo
    Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
  • E. Tallimba
    Tallimba is a small rural locality in the Riverina region of New South Wales, Australia, situated within an agricultural area west of the town of West Wyalong.
  • 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_69c008b1c5088190ae6de2555fc05ad8 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0631b32308190a8211043d1caa6e6 completed March 22, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69c20e12fa248190ad9daaf9563d38c6 completed March 24, 2026, 4:07 a.m.
NEDg Description generation batch_69c214aaef308190be1166c1389bf3d3 completed March 24, 2026, 4:35 a.m.
NED2 Entity disambiguation (via description) batch_69c21508dbec8190b9bb4806a83ecb13 completed March 24, 2026, 4:37 a.m.
Created at: March 22, 2026, 4:23 p.m.