Triple

T19478613
Position Surface form Disambiguated ID Type / Status
Subject Names of Singapore E487318 entity
Predicate hasName P744 FINISHED
Object Syonan
Syonan was the name used by Imperial Japan for Singapore during its World War II occupation from 1942 to 1945.
 E1380494 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: Syonan | Statement: [Names of Singapore, hasName, Syonan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Syonan
Context triple: [Names of Singapore, hasName, Syonan]
  • A. Syon
    Syon is a historic district in west London best known for Syon House and its surrounding parkland, the London home of the Duke of Northumberland.
  • B. Sennan
    Sennan is a coastal city in Osaka Prefecture, Japan, known for its proximity to Kansai International Airport and its role as part of the greater Osaka metropolitan area.
  • C. Nagaya
    Nagaya is a Japanese surname historically borne by various notable figures, including samurai and aristocrats, and remains in use in modern Japan.
  • D. Sinnai
    Sinnai is a town and comune in southern Sardinia, Italy, known for its traditional culture and proximity to the island’s capital, Cagliari.
  • E. Syeni
    Syeni is a figure from Hindu mythology known primarily as one of the wives of the sage Kashyapa.
  • 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: Syonan
Triple: [Names of Singapore, hasName, Syonan]
Generated description
Syonan was the name used by Imperial Japan for Singapore during its World War II occupation from 1942 to 1945.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Syonan
Target entity description: Syonan was the name used by Imperial Japan for Singapore during its World War II occupation from 1942 to 1945.
  • A. Syon
    Syon is a historic district in west London best known for Syon House and its surrounding parkland, the London home of the Duke of Northumberland.
  • B. Sennan
    Sennan is a coastal city in Osaka Prefecture, Japan, known for its proximity to Kansai International Airport and its role as part of the greater Osaka metropolitan area.
  • C. Nagaya
    Nagaya is a Japanese surname historically borne by various notable figures, including samurai and aristocrats, and remains in use in modern Japan.
  • D. Sinnai
    Sinnai is a town and comune in southern Sardinia, Italy, known for its traditional culture and proximity to the island’s capital, Cagliari.
  • E. Syeni
    Syeni is a figure from Hindu mythology known primarily as one of the wives of the sage Kashyapa.
  • 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_69d8e8d924388190b847cb15bb3d0aff completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63437b9748190a8fc6bf6b3d90918 completed April 20, 2026, 2:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0747136a7c8190be4a8b197e47daf8 completed May 15, 2026, 4:17 p.m.
NEDg Description generation batch_6a0749b0c40c81908127b8e69d822706 completed May 15, 2026, 4:28 p.m.
NED2 Entity disambiguation (via description) batch_6a074a2e57508190aceac1c48ee3af8c completed May 15, 2026, 4:30 p.m.
Created at: April 10, 2026, 1:39 p.m.