Triple

T11349299
Position Surface form Disambiguated ID Type / Status
Subject Mai-Mai militias E268798 entity
Predicate hasSubgroup P747 FINISHED
Object Mai-Mai Simba
Mai-Mai Simba is a Congolese militia faction known for its involvement in local armed conflicts and community-based self-defense activities in eastern Democratic Republic of the Congo.
E920234 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: Mai-Mai Simba | Statement: [Mai-Mai militias, hasSubgroup, Mai-Mai Simba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mai-Mai Simba
Context triple: [Mai-Mai militias, hasSubgroup, Mai-Mai Simba]
  • A. Zizi
    Zizi is a Liberian former professional footballer best known for playing as a striker for clubs in Europe and the United States.
  • B. Pakpak Simsim
    Pakpak Simsim is a regional dialect of the Pakpak Dairi language spoken by the Pakpak people of northern Sumatra, Indonesia.
  • C. Mamo
    Mamo is a Maltese surname most notably borne by Sir Anthony Mamo, the first President of Malta.
  • D. Mambae
    Mambae is an Austronesian language spoken primarily in the central and eastern regions of Timor-Leste.
  • E. Kiko
    Kiko is the Crown Princess of Japan and the wife of Crown Prince Akishino, a prominent member of the Japanese imperial family.
  • 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: Mai-Mai Simba
Triple: [Mai-Mai militias, hasSubgroup, Mai-Mai Simba]
Generated description
Mai-Mai Simba is a Congolese militia faction known for its involvement in local armed conflicts and community-based self-defense activities in eastern Democratic Republic of the Congo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mai-Mai Simba
Target entity description: Mai-Mai Simba is a Congolese militia faction known for its involvement in local armed conflicts and community-based self-defense activities in eastern Democratic Republic of the Congo.
  • A. Zizi
    Zizi is a Liberian former professional footballer best known for playing as a striker for clubs in Europe and the United States.
  • B. Pakpak Simsim
    Pakpak Simsim is a regional dialect of the Pakpak Dairi language spoken by the Pakpak people of northern Sumatra, Indonesia.
  • C. Mamo
    Mamo is a Maltese surname most notably borne by Sir Anthony Mamo, the first President of Malta.
  • D. Mambae
    Mambae is an Austronesian language spoken primarily in the central and eastern regions of Timor-Leste.
  • E. Kiko
    Kiko is the Crown Princess of Japan and the wife of Crown Prince Akishino, a prominent member of the Japanese imperial family.
  • 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_69d6aacbe18081909e5fadb50082dd96 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea23391c819089e8f9725cb3a0ff completed April 9, 2026, 6:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5438d7b58819093cc1407fefe8ab5 completed April 19, 2026, 9:05 p.m.
NEDg Description generation batch_69e548bb7be4819093aeeaf0c048033e completed April 19, 2026, 9:27 p.m.
NED2 Entity disambiguation (via description) batch_69e54eeba4a88190af128a99c277853a completed April 19, 2026, 9:53 p.m.
Created at: April 8, 2026, 9:33 p.m.