Triple

T8335239
Position Surface form Disambiguated ID Type / Status
Subject Iga E195170 entity
Predicate hasAlias P455 FINISHED
Object Iga-shi
Iga-shi is a city in Mie Prefecture, Japan, historically famous as a center of ninja (shinobi) culture and training.
 E728268 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: Iga-shi | Statement: [Iga, hasAlias, Iga-shi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Iga-shi
Context triple: [Iga, hasAlias, Iga-shi]
  • A. Ōgaki
    Ōgaki is a former municipality in Hiroshima Prefecture, Japan, that was incorporated into the city of Etajima.
  • B. Kakogawa
    Kakogawa is an industrial and residential city in central Hyōgo Prefecture, Japan, known for its steel manufacturing and role as a regional transportation hub.
  • C. Ichigaya
    Ichigaya is a central Tokyo district known for its major railway station, government and educational institutions, and proximity to the Imperial Palace area.
  • D. Izuhara
    Izuhara is the main town and administrative center of Tsushima Island in Nagasaki Prefecture, Japan.
  • E. Higashiyamato
    Higashiyamato is a suburban city in western Tokyo, Japan, known for its residential neighborhoods and proximity to the Tama region’s parks and green spaces.
  • 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: Iga-shi
Triple: [Iga, hasAlias, Iga-shi]
Generated description
Iga-shi is a city in Mie Prefecture, Japan, historically famous as a center of ninja (shinobi) culture and training.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Iga-shi
Target entity description: Iga-shi is a city in Mie Prefecture, Japan, historically famous as a center of ninja (shinobi) culture and training.
  • A. Ōgaki
    Ōgaki is a former municipality in Hiroshima Prefecture, Japan, that was incorporated into the city of Etajima.
  • B. Kakogawa
    Kakogawa is an industrial and residential city in central Hyōgo Prefecture, Japan, known for its steel manufacturing and role as a regional transportation hub.
  • C. Ichigaya
    Ichigaya is a central Tokyo district known for its major railway station, government and educational institutions, and proximity to the Imperial Palace area.
  • D. Izuhara
    Izuhara is the main town and administrative center of Tsushima Island in Nagasaki Prefecture, Japan.
  • E. Higashiyamato
    Higashiyamato is a suburban city in western Tokyo, Japan, known for its residential neighborhoods and proximity to the Tama region’s parks and green spaces.
  • 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_69ca82e87f2c8190bdb71ee29dfc642d completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7fd2ca648190991e398ba70caf8d completed March 31, 2026, 8:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc71005f0819092c46fa5a43f2a15 completed April 2, 2026, 1:32 a.m.
NEDg Description generation batch_69cdcb90bec88190a2c19681405aa13e completed April 2, 2026, 1:51 a.m.
NED2 Entity disambiguation (via description) batch_69cdcd0fc9488190a0a576c385b9bc1f completed April 2, 2026, 1:57 a.m.
Created at: March 30, 2026, 5:57 p.m.