Triple

T9537021
Position Surface form Disambiguated ID Type / Status
Subject Laïty Kama E230043 entity
Predicate familyName P18 FINISHED
Object Kama
Kama is a surname of likely West African origin, notably borne by figures such as Senegalese jurist and former International Criminal Tribunal judge Laïty Kama.
 E805640 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: Kama | Statement: [Laïty Kama, familyName, Kama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kama
Context triple: [Laïty Kama, familyName, Kama]
  • A. Kama
    Kama is a small city located in Japan’s Fukuoka Prefecture on the island of Kyushu.
  • B. Kaa
    Kaa is a giant, hypnotic python who serves as a dangerous and manipulative predator in Disney’s live-action adaptation of The Jungle Book.
  • C. Tantamani
    Tantamani was a Kushite king of the 25th Dynasty of Egypt, known for his brief attempt to restore Nubian control over Egypt before being driven back by the Assyrians.
  • D. Maasim
    Maasim is a coastal municipality in the province of South Cotabato on the island of Mindanao in the Philippines, known for agriculture and fishing.
  • E. Kameari
    Kameari is a neighborhood in Tokyo best known as the long-running setting of the manga and anime series "Kochira Katsushika-ku Kameari Kōen-mae Hashutsujo" ("Kochikame").
  • 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: Kama
Triple: [Laïty Kama, familyName, Kama]
Generated description
Kama is a surname of likely West African origin, notably borne by figures such as Senegalese jurist and former International Criminal Tribunal judge Laïty Kama.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kama
Target entity description: Kama is a surname of likely West African origin, notably borne by figures such as Senegalese jurist and former International Criminal Tribunal judge Laïty Kama.
  • A. Kama
    Kama is a small city located in Japan’s Fukuoka Prefecture on the island of Kyushu.
  • B. Kaa
    Kaa is a giant, hypnotic python who serves as a dangerous and manipulative predator in Disney’s live-action adaptation of The Jungle Book.
  • C. Tantamani
    Tantamani was a Kushite king of the 25th Dynasty of Egypt, known for his brief attempt to restore Nubian control over Egypt before being driven back by the Assyrians.
  • D. Maasim
    Maasim is a coastal municipality in the province of South Cotabato on the island of Mindanao in the Philippines, known for agriculture and fishing.
  • E. Kameari
    Kameari is a neighborhood in Tokyo best known as the long-running setting of the manga and anime series "Kochira Katsushika-ku Kameari Kōen-mae Hashutsujo" ("Kochikame").
  • 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_69ca847b1b3081908f72bc932c17cc41 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd98ce884c8190a8b3c2dc7c73c2c9 completed April 1, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c4f1fc08190a1ad3d862717eef3 completed April 4, 2026, 5:37 p.m.
NEDg Description generation batch_69d14d44b7f08190b66fecb315b37535 completed April 4, 2026, 5:41 p.m.
NED2 Entity disambiguation (via description) batch_69d14e0823e881908ed723d20f14789b completed April 4, 2026, 5:44 p.m.
Created at: March 30, 2026, 8:01 p.m.