Triple

T25712274
Position Surface form Disambiguated ID Type / Status
Subject Cầu quay Sông Hàn E644764 entity
Predicate locatedInAdministrativeTerritory P40 FINISHED
Object Sơn Trà District
Sơn Trà District is a coastal urban district of Da Nang, Vietnam, known for its beaches, the Sơn Trà Peninsula nature reserve, and key city landmarks.
E1838142 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: Sơn Trà District | Statement: [Cầu quay Sông Hàn, locatedInAdministrativeTerritory, Sơn Trà District]
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: Sơn Trà District
Triple: [Cầu quay Sông Hàn, locatedInAdministrativeTerritory, Sơn Trà District]
Generated description
Sơn Trà District is a coastal urban district of Da Nang, Vietnam, known for its beaches, the Sơn Trà Peninsula nature reserve, and key city landmarks.

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_69e77e83c8ec8190bf52fcdac4838984 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fc163fe48190a2680fa21dee8838 completed May 2, 2026, 1:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24d3d04e9481908cd18c5fc4b239e9 completed June 7, 2026, 2:13 a.m.
NEDg Description generation batch_6a24d7f48c948190b614235728863682 completed June 7, 2026, 2:31 a.m.
NED2 Entity disambiguation (via description) batch_6a24da02305081908055992ee6c0fc56 completed June 7, 2026, 2:40 a.m.
Created at: April 21, 2026, 9:17 p.m.