Triple

T35057549
Position Surface form Disambiguated ID Type / Status
Subject Songshan District, Taipei, Taiwan E1011507 entity
Predicate hasLandmark P105 FINISHED
Object Taipei Municipal Stadium
Taipei Municipal Stadium is a major multi-purpose sports venue in Taipei known primarily for hosting athletics events, football matches, and large-scale public gatherings.
E2125831 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: Taipei Municipal Stadium | Statement: [Songshan District, Taipei, Taiwan, hasLandmark, Taipei Municipal Stadium]
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: Taipei Municipal Stadium
Triple: [Songshan District, Taipei, Taiwan, hasLandmark, Taipei Municipal Stadium]
Generated description
Taipei Municipal Stadium is a major multi-purpose sports venue in Taipei known primarily for hosting athletics events, football matches, and large-scale public gatherings.

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_69f76dd09c308190a523454853ce842b completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f785d2233881909b0b1d604db44e53 completed May 3, 2026, 5:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37cfe198fc81909bea21d760c5a0f7 completed June 21, 2026, 11:49 a.m.
NEDg Description generation batch_6a37d07ba708819081b552b8a69313e5 completed June 21, 2026, 11:52 a.m.
NED2 Entity disambiguation (via description) batch_6a37d1935d9881909cee3755fec2d996 completed June 21, 2026, 11:57 a.m.
Created at: May 3, 2026, 4:01 p.m.