Triple

T879432
Position Surface form Disambiguated ID Type / Status
Subject Madhya Pradesh E18993 entity
Predicate hasMajorCity P316 FINISHED
Object Morena
Morena is a city in the northern part of the Indian state of Madhya Pradesh, known as an administrative and commercial center in the Chambal region.
 E102805 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: Morena | Statement: [Madhya Pradesh, hasMajorCity, Morena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Morena
Context triple: [Madhya Pradesh, hasMajorCity, Morena]
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Minervina
    Minervina was the first wife or consort of the Roman emperor Constantine the Great, known primarily as the mother of his son Crispus.
  • C. Rosaura
    Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • D. Alejandro
    Alejandro is the Spanish form of the given name Alexander, commonly used in Spanish-speaking countries.
  • E. Máxima
    Máxima is the Argentine-born Queen consort of the Netherlands, married to King Willem-Alexander and known for her work in finance, social inclusion, and microcredit initiatives.
  • 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: Morena
Triple: [Madhya Pradesh, hasMajorCity, Morena]
Generated description
Morena is a city in the northern part of the Indian state of Madhya Pradesh, known as an administrative and commercial center in the Chambal region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Morena
Target entity description: Morena is a city in the northern part of the Indian state of Madhya Pradesh, known as an administrative and commercial center in the Chambal region.
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Minervina
    Minervina was the first wife or consort of the Roman emperor Constantine the Great, known primarily as the mother of his son Crispus.
  • C. Rosaura
    Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • D. Alejandro
    Alejandro is the Spanish form of the given name Alexander, commonly used in Spanish-speaking countries.
  • E. Máxima
    Máxima is the Argentine-born Queen consort of the Netherlands, married to King Willem-Alexander and known for her work in finance, social inclusion, and microcredit initiatives.
  • 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_69a4938db1f081909bcd1ad2713b6096 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4acc9d5f4819087afbb75b6ac3dbf completed March 1, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7b85630f081909f3a912d54dd328c completed March 4, 2026, 4:43 a.m.
NEDg Description generation batch_69a7b8de773c8190a993b057dfe3693e completed March 4, 2026, 4:45 a.m.
NED2 Entity disambiguation (via description) batch_69a7b9793dd48190869b3499fe170efd completed March 4, 2026, 4:47 a.m.
Created at: March 1, 2026, 7:39 p.m.