Triple

T30804115
Position Surface form Disambiguated ID Type / Status
Subject Trường Chinh Street E784448 entity
Predicate hasNameInLatinAlphabet P22444 FINISHED
Object Truong Chinh Street
Truong Chinh Street is a major urban thoroughfare in Hanoi, Vietnam, known for heavy traffic and ongoing infrastructure development.
 E1795632 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: Truong Chinh Street | Statement: [Trường Chinh Street, hasNameInLatinAlphabet, Truong Chinh Street]
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: Truong Chinh Street
Triple: [Trường Chinh Street, hasNameInLatinAlphabet, Truong Chinh Street]
Generated description
Truong Chinh Street is a major urban thoroughfare in Hanoi, Vietnam, known for heavy traffic and ongoing infrastructure development.

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_69f224b3a7ec819096939414d103e31e completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6903d362081909535bb042ea5f111 completed May 3, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad2146d8c8190bb8571261ede4dca completed June 11, 2026, 3:19 p.m.
NEDg Description generation batch_6a2ae91388e8819093a8f184e2273c44 completed June 11, 2026, 4:57 p.m.
NED2 Entity disambiguation (via description) batch_6a2aee8d2e10819082ce03bf0d1c8d2b completed June 11, 2026, 5:21 p.m.
Created at: April 29, 2026, 8:42 p.m.