Triple

T12952942
Position Surface form Disambiguated ID Type / Status
Subject Battle of Teba E309937 entity
Predicate location P40 FINISHED
Object Teba
Teba is a town in the province of Málaga, Spain, historically notable as the site of a major medieval battle during the Reconquista.
 E1010226 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: Teba | Statement: [Battle of Teba, location, Teba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teba
Context triple: [Battle of Teba, location, Teba]
  • A. Chemelil
    Chemelil is a town in western Kenya known primarily for its large sugar industry and the Chemelil Sugar Company.
  • B. Bawi
    Bawi was a Sasanian Persian military commander known for leading forces against the Byzantine Empire during the Iberian War in the 6th century.
  • C. Habur
    Habur is the ancient name of the Khabur River, a historically significant tributary of the Euphrates in northern Mesopotamia.
  • D. Noor
    Noor is the American-born widow of King Hussein who served as Queen consort of Jordan and became known for her humanitarian and peace-building work.
  • E. Noor
    Noor is a science fiction novel by Nnedi Okorafor that blends Africanfuturism with themes of identity, technology, and survival in a near-future Nigeria.
  • 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: Teba
Triple: [Battle of Teba, location, Teba]
Generated description
Teba is a town in the province of Málaga, Spain, historically notable as the site of a major medieval battle during the Reconquista.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teba
Target entity description: Teba is a town in the province of Málaga, Spain, historically notable as the site of a major medieval battle during the Reconquista.
  • A. Chemelil
    Chemelil is a town in western Kenya known primarily for its large sugar industry and the Chemelil Sugar Company.
  • B. Bawi
    Bawi was a Sasanian Persian military commander known for leading forces against the Byzantine Empire during the Iberian War in the 6th century.
  • C. Habur
    Habur is the ancient name of the Khabur River, a historically significant tributary of the Euphrates in northern Mesopotamia.
  • D. Noor
    Noor is the American-born widow of King Hussein who served as Queen consort of Jordan and became known for her humanitarian and peace-building work.
  • E. Noor
    Noor is a science fiction novel by Nnedi Okorafor that blends Africanfuturism with themes of identity, technology, and survival in a near-future Nigeria.
  • 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_69d7bdfb57a88190836b743e2825feca completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97e1edcdc8190a702c2a5ea58cc67 completed April 10, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af7a10f48190b7e0d32725f83fb6 completed May 3, 2026, 2:14 a.m.
NEDg Description generation batch_69f6b02f194c8190921aee4cb016a6e2 completed May 3, 2026, 2:17 a.m.
NED2 Entity disambiguation (via description) batch_69f6b0928ef48190ab4240abbccec5f2 completed May 3, 2026, 2:18 a.m.
Created at: April 9, 2026, 5:44 p.m.