Triple

T20180532
Position Surface form Disambiguated ID Type / Status
Subject Damoh district E492713 entity
Predicate hasMajorTown P316 FINISHED
Object Hatta
Hatta is a prominent town in the Damoh district of Madhya Pradesh, India, known as a local commercial and administrative center.
 E1417270 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: Hatta | Statement: [Damoh district, hasMajorTown, Hatta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hatta
Context triple: [Damoh district, hasMajorTown, Hatta]
  • A. Hatta
    Hatta is an Indonesian surname most prominently associated with Mohammad Hatta, the country’s first vice president and a leading figure in the struggle for independence.
  • B. Haruru
    Haruru is a small settlement in New Zealand’s Bay of Islands region, known for its scenic surroundings and proximity to Haruru Falls.
  • C. Moru
    Moru is a Central Sudanic language spoken primarily by the Moru people in South Sudan.
  • D. Kahama
    Kahama is a town and district-level administrative center in northwestern Tanzania known for its mining activities and role as a commercial hub in the Shinyanga area.
  • E. Gohatto
    Gohatto is a 1999 Japanese period drama film directed by Nagisa Ōshima that explores forbidden desire and tensions within the samurai ranks of the Shinsengumi.
  • 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: Hatta
Triple: [Damoh district, hasMajorTown, Hatta]
Generated description
Hatta is a prominent town in the Damoh district of Madhya Pradesh, India, known as a local commercial and administrative center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hatta
Target entity description: Hatta is a prominent town in the Damoh district of Madhya Pradesh, India, known as a local commercial and administrative center.
  • A. Hatta
    Hatta is an Indonesian surname most prominently associated with Mohammad Hatta, the country’s first vice president and a leading figure in the struggle for independence.
  • B. Haruru
    Haruru is a small settlement in New Zealand’s Bay of Islands region, known for its scenic surroundings and proximity to Haruru Falls.
  • C. Moru
    Moru is a Central Sudanic language spoken primarily by the Moru people in South Sudan.
  • D. Kahama
    Kahama is a town and district-level administrative center in northwestern Tanzania known for its mining activities and role as a commercial hub in the Shinyanga area.
  • E. Gohatto
    Gohatto is a 1999 Japanese period drama film directed by Nagisa Ōshima that explores forbidden desire and tensions within the samurai ranks of the Shinsengumi.
  • 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_69da6268a034819081cbd9ea5a1c9475 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e668eed2e88190b54b15e6545dbdf8 completed April 20, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a083c7b9fa08190bbf3c3da21ab21ba completed May 16, 2026, 9:44 a.m.
NEDg Description generation batch_6a084094eccc8190834a1335f3b70411 completed May 16, 2026, 10:01 a.m.
NED2 Entity disambiguation (via description) batch_6a0841a3f8e0819099c13e76d5f4285d completed May 16, 2026, 10:06 a.m.
Created at: April 11, 2026, 11:36 p.m.