Triple

T37063654
Position Surface form Disambiguated ID Type / Status
Subject Deputy Prime Minister of Malaysia E917385 entity
Predicate officeHolders P9949 FINISHED
Object Musa Hitam
Musa Hitam is a Malaysian politician best known for serving as a prominent national leader during the Mahathir era, playing a key role in the country’s political and economic development in the 1980s.
E2210002 NE FINISHED

How this triple was built (2 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: Musa Hitam | Statement: [Deputy Prime Minister of Malaysia, officeHolders, Musa Hitam]
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: Musa Hitam
Triple: [Deputy Prime Minister of Malaysia, officeHolders, Musa Hitam]
Generated description
Musa Hitam is a Malaysian politician best known for serving as a prominent national leader during the Mahathir era, playing a key role in the country’s political and economic development in the 1980s.

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_69f76e95fa40819091e14681087ae5e4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb2f8eeb388190afdc8cfd2cc3e5a6 completed May 6, 2026, 12:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e8c5715e8819082c99d589abd3baa completed June 26, 2026, 2:27 p.m.
NEDg Description generation batch_6a3e95ede37c8190976559fecd7917b3 completed June 26, 2026, 3:08 p.m.
NED2 Entity disambiguation (via description) batch_6a3e9914a3e48190bf59606bacf80c87 completed June 26, 2026, 3:21 p.m.
Created at: May 3, 2026, 4:14 p.m.