Triple

T1853046
Position Surface form Disambiguated ID Type / Status
Subject Kelantan E41638 entity
Predicate hasTown P847 FINISHED
Object Tumpat
Tumpat is a coastal town and district in northeastern Kelantan, Malaysia, known for its fishing communities and role as a transport hub near the Thai border.
 E207469 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: Tumpat | Statement: [Kelantan, hasTown, Tumpat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tumpat
Context triple: [Kelantan, hasTown, Tumpat]
  • A. Tigak
    Tigak is an Austronesian language of the Meso-Melanesian subgroup spoken primarily in parts of Papua New Guinea.
  • B. Pekat
    Pekat is a settlement on the Indonesian island of Sumbawa that was devastated by the catastrophic 1815 eruption of Mount Tambora.
  • C. Tamambo
    Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
  • D. Napareuli
    Napareuli is a Georgian wine appellation in the Kakheti region, known for producing high-quality wines, particularly from the Saperavi grape.
  • E. Batu
    Batu is a highland city in East Java, Indonesia, known for its cool climate, mountain scenery, and popular tourist attractions such as theme parks and apple orchards.
  • 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: Tumpat
Triple: [Kelantan, hasTown, Tumpat]
Generated description
Tumpat is a coastal town and district in northeastern Kelantan, Malaysia, known for its fishing communities and role as a transport hub near the Thai border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tumpat
Target entity description: Tumpat is a coastal town and district in northeastern Kelantan, Malaysia, known for its fishing communities and role as a transport hub near the Thai border.
  • A. Tigak
    Tigak is an Austronesian language of the Meso-Melanesian subgroup spoken primarily in parts of Papua New Guinea.
  • B. Pekat
    Pekat is a settlement on the Indonesian island of Sumbawa that was devastated by the catastrophic 1815 eruption of Mount Tambora.
  • C. Tamambo
    Tamambo is an Oceanic Austronesian language spoken primarily on Malo Island in Vanuatu.
  • D. Napareuli
    Napareuli is a Georgian wine appellation in the Kakheti region, known for producing high-quality wines, particularly from the Saperavi grape.
  • E. Batu
    Batu is a highland city in East Java, Indonesia, known for its cool climate, mountain scenery, and popular tourist attractions such as theme parks and apple orchards.
  • 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_69a8864a83848190a4ec02721306c511 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb06999f4819086386aafb789a368 completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1c89bdc8190acf517a7731fa5c7 completed March 8, 2026, 7:45 p.m.
NEDg Description generation batch_69add25c9c208190a576cf1123c0a2e6 completed March 8, 2026, 7:47 p.m.
NED2 Entity disambiguation (via description) batch_69add32803a48190b8ff08c605790f22 completed March 8, 2026, 7:51 p.m.
Created at: March 4, 2026, 7:33 p.m.