Triple

T7901161
Position Surface form Disambiguated ID Type / Status
Subject Central Sama E183454 entity
Predicate hasDialect P4251 FINISHED
Object Sitangkai Sama
Sitangkai Sama is a dialect of the Central Sama language spoken primarily by Sama communities in and around Sitangkai in the southern Philippines.
E700460 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: Sitangkai Sama | Statement: [Central Sama, hasDialect, Sitangkai Sama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sitangkai Sama
Context triple: [Central Sama, hasDialect, Sitangkai Sama]
  • A. Laksamana
    Laksamana is a prominent heroic figure in Malay literary tradition, often depicted as a loyal and valiant warrior and royal commander.
  • B. Galang
    Galang is an island in Indonesia’s Riau Islands province, historically known for hosting a Vietnamese refugee camp during the late 20th century.
  • C. Pakpak Keppas
    Pakpak Keppas is a regional dialect of the Pakpak Dairi language spoken by the Pakpak ethnic community in parts of North Sumatra, Indonesia.
  • D. Bantia
    Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
  • E. Rangloi
    Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
  • 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: Sitangkai Sama
Triple: [Central Sama, hasDialect, Sitangkai Sama]
Generated description
Sitangkai Sama is a dialect of the Central Sama language spoken primarily by Sama communities in and around Sitangkai in the southern Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sitangkai Sama
Target entity description: Sitangkai Sama is a dialect of the Central Sama language spoken primarily by Sama communities in and around Sitangkai in the southern Philippines.
  • A. Laksamana
    Laksamana is a prominent heroic figure in Malay literary tradition, often depicted as a loyal and valiant warrior and royal commander.
  • B. Galang
    Galang is an island in Indonesia’s Riau Islands province, historically known for hosting a Vietnamese refugee camp during the late 20th century.
  • C. Pakpak Keppas
    Pakpak Keppas is a regional dialect of the Pakpak Dairi language spoken by the Pakpak ethnic community in parts of North Sumatra, Indonesia.
  • D. Bantia
    Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
  • E. Rangloi
    Rangloi is a regional dialect of the Kumaoni language spoken in parts of the Indian Himalayan region.
  • 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_69ca828d13088190b222be7aa9f9315c completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3a3f4c2c81909ae70b0acf4729be completed March 31, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5bbd93348190883c6152f18f8214 completed March 31, 2026, 5:29 a.m.
NEDg Description generation batch_69cb7632cbbc819087107c8d2172a038 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb64eee408190a66cbd0cba3054b4 completed March 31, 2026, 11:55 a.m.
Created at: March 30, 2026, 5:02 p.m.