Triple

T15269247
Position Surface form Disambiguated ID Type / Status
Subject Rembarrnga E364976 entity
Predicate hasNeighboringLanguage P16383 FINISHED
Object Dalabon
Dalabon is an Australian Aboriginal language traditionally spoken in Arnhem Land in the Northern Territory, now critically endangered with only a few remaining speakers.
 E1147181 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: Dalabon | Statement: [Rembarrnga, hasNeighboringLanguage, Dalabon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dalabon
Context triple: [Rembarrnga, hasNeighboringLanguage, Dalabon]
  • A. Labuha
    Labuha is a coastal town that serves as an important local center on the Indonesian island of Halmahera.
  • B. Kalabahi
    Kalabahi is the main town and administrative center on Alor Island in Indonesia’s East Nusa Tenggara province.
  • C. Malabuyoc
    Malabuyoc is a coastal municipality in the southwestern part of Cebu province in the Philippines, known for its hot springs and scenic seaside landscapes.
  • D. Dalabanan
    Dalabanan is a Swedish railway line that connects Uppsala with the Dalarna region, serving as an important route for both passenger and regional traffic.
  • E. Dadiangas
    Dadiangas is the former name of General Santos, a major city in the southern Philippines known for its tuna fishing industry.
  • 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: Dalabon
Triple: [Rembarrnga, hasNeighboringLanguage, Dalabon]
Generated description
Dalabon is an Australian Aboriginal language traditionally spoken in Arnhem Land in the Northern Territory, now critically endangered with only a few remaining speakers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dalabon
Target entity description: Dalabon is an Australian Aboriginal language traditionally spoken in Arnhem Land in the Northern Territory, now critically endangered with only a few remaining speakers.
  • A. Labuha
    Labuha is a coastal town that serves as an important local center on the Indonesian island of Halmahera.
  • B. Kalabahi
    Kalabahi is the main town and administrative center on Alor Island in Indonesia’s East Nusa Tenggara province.
  • C. Malabuyoc
    Malabuyoc is a coastal municipality in the southwestern part of Cebu province in the Philippines, known for its hot springs and scenic seaside landscapes.
  • D. Dalabanan
    Dalabanan is a Swedish railway line that connects Uppsala with the Dalarna region, serving as an important route for both passenger and regional traffic.
  • E. Dadiangas
    Dadiangas is the former name of General Santos, a major city in the southern Philippines known for its tuna fishing industry.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0094ca9ac8190a1f97a7b74c96cd5 completed April 15, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee602067c81908b1aaaca8871eeb9 completed May 9, 2026, 7:45 a.m.
NEDg Description generation batch_69fee6b1a29481908c5c945ef801468d completed May 9, 2026, 7:48 a.m.
NED2 Entity disambiguation (via description) batch_69fee7b5e3f0819091246455e239996a completed May 9, 2026, 7:52 a.m.
Created at: April 10, 2026, 3:14 a.m.