Triple

T32934176

Position	Surface form	Disambiguated ID	Type / Status
Subject	Negeri Sembilan royal institutions	`E842482`	entity
Predicate	hasKeyOffice	`P1268`	FINISHED
Object	Tunku Besar of Tampin The Tunku Besar of Tampin is a hereditary Malay royal title and ruler of the Tampin district within the Negeri Sembilan monarchy in Malaysia.	`E2286473`	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: Tunku Besar of Tampin | Statement: [Negeri Sembilan royal institutions, hasKeyOffice, Tunku Besar of Tampin]

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: Tunku Besar of Tampin
Triple: [Negeri Sembilan royal institutions, hasKeyOffice, Tunku Besar of Tampin]

Generated description

The Tunku Besar of Tampin is a hereditary Malay royal title and ruler of the Tampin district within the Negeri Sembilan monarchy in Malaysia.

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_69f34948adfc8190a937f1f622783c0b`	completed	April 30, 2026, 12:21 p.m.
NER	Named-entity recognition	`batch_69f6d108f2008190be705c3372b95fe5`	completed	May 3, 2026, 4:37 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a46b86118408190bb0fa8ca951236d3`	completed	July 2, 2026, 7:13 p.m.
NEDg	Description generation	`batch_6a46b8e5c5188190b49f4a72402624ad`	completed	July 2, 2026, 7:15 p.m.
NED2	Entity disambiguation (via description)	`batch_6a46b9a584448190b59616e60779ec6a`	completed	July 2, 2026, 7:19 p.m.

Created at: May 1, 2026, 1:20 a.m.