Triple

T1783147
Position Surface form Disambiguated ID Type / Status
Subject Perak E39332 entity
Predicate containsTown P847 FINISHED
Object Lumut
Lumut is a coastal town in the Malaysian state of Perak, known as a gateway to Pangkor Island and as a naval and port town.
 E206262 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: Lumut | Statement: [Perak, containsTown, Lumut]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lumut
Context triple: [Perak, containsTown, Lumut]
  • A. Kertajaya
    Kertajaya was a 13th-century king of the Kediri Kingdom in Java, remembered for his conflict with the emerging Singhasari kingdom and his role in the region’s political transition.
  • B. Kedah
    Kedah is a state in northwestern Peninsular Malaysia, historically significant as one of the oldest Malay kingdoms and once part of British Malaya.
  • C. Johor Lama
    Johor Lama was a historic fortified riverine settlement that served as an important political and trading center of the Johor Sultanate in the Malay Peninsula.
  • D. Ipoh
    Ipoh is a prominent city in northwestern Peninsular Malaysia, known for its colonial-era architecture, limestone hills and caves, and vibrant food scene.
  • E. 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.
  • 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: Lumut
Triple: [Perak, containsTown, Lumut]
Generated description
Lumut is a coastal town in the Malaysian state of Perak, known as a gateway to Pangkor Island and as a naval and port town.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lumut
Target entity description: Lumut is a coastal town in the Malaysian state of Perak, known as a gateway to Pangkor Island and as a naval and port town.
  • A. Kertajaya
    Kertajaya was a 13th-century king of the Kediri Kingdom in Java, remembered for his conflict with the emerging Singhasari kingdom and his role in the region’s political transition.
  • B. Kedah
    Kedah is a state in northwestern Peninsular Malaysia, historically significant as one of the oldest Malay kingdoms and once part of British Malaya.
  • C. Johor Lama
    Johor Lama was a historic fortified riverine settlement that served as an important political and trading center of the Johor Sultanate in the Malay Peninsula.
  • D. Ipoh
    Ipoh is a prominent city in northwestern Peninsular Malaysia, known for its colonial-era architecture, limestone hills and caves, and vibrant food scene.
  • E. 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.
  • 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_69a88630519c8190a17addd83c4a3ef4 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64e4cf108190891338052b581ae8 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9a4ee9c8190a6cdb5df16a48711 completed March 8, 2026, 7:10 p.m.
NEDg Description generation batch_69adcc272dbc81909d1f9b007ba19448 completed March 8, 2026, 7:21 p.m.
NED2 Entity disambiguation (via description) batch_69adcc9b53c4819090cf0659377c001b completed March 8, 2026, 7:23 p.m.
Created at: March 4, 2026, 7:31 p.m.