Triple

T29376202
Position Surface form Disambiguated ID Type / Status
Subject Nguyễn Văn Cừ Street E745003 entity
Predicate namedAfter P63 FINISHED
Object Nguyễn Văn Cừ
Nguyễn Văn Cừ was a prominent Vietnamese revolutionary and early General Secretary of the Indochinese Communist Party who played a key role in the anti-colonial struggle against French rule.
E1868346 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: Nguyễn Văn Cừ | Statement: [Nguyễn Văn Cừ Street, namedAfter, Nguyễn Văn Cừ]
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: Nguyễn Văn Cừ
Triple: [Nguyễn Văn Cừ Street, namedAfter, Nguyễn Văn Cừ]
Generated description
Nguyễn Văn Cừ was a prominent Vietnamese revolutionary and early General Secretary of the Indochinese Communist Party who played a key role in the anti-colonial struggle against French rule.

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_69f0a79ba954819094597628112c6091 completed April 28, 2026, 12:27 p.m.
NER Named-entity recognition batch_69f669aeb2208190b5b578c5d94edb63 completed May 2, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25f0fbcfcc8190b76da4050a9ac317 completed June 7, 2026, 10:30 p.m.
NEDg Description generation batch_6a25f6362f6081909a04ef3fbd5bb67f completed June 7, 2026, 10:52 p.m.
NED2 Entity disambiguation (via description) batch_6a25fa9d98d08190aef6fb0a1779f501 completed June 7, 2026, 11:11 p.m.
Created at: April 28, 2026, 2:31 p.m.