Triple

T25182108
Position Surface form Disambiguated ID Type / Status
Subject Hagia Triada Greek Orthodox Church E630608 entity
Predicate isLandmarkOf P6629 FINISHED
Object Taksim area
The Taksim area is a central, bustling district of Istanbul known for its major square, shopping and nightlife along İstiklal Avenue, and its mix of historic landmarks and modern urban life.
 E1665288 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: Taksim area | Statement: [Hagia Triada Greek Orthodox Church, isLandmarkOf, Taksim area]
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: Taksim area
Triple: [Hagia Triada Greek Orthodox Church, isLandmarkOf, Taksim area]
Generated description
The Taksim area is a central, bustling district of Istanbul known for its major square, shopping and nightlife along İstiklal Avenue, and its mix of historic landmarks and modern urban life.

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_69e75a88fdf081908e47ae6e195c14e1 completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f46dc784448190ba2ef45b1d688ec9 completed May 1, 2026, 9:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d1e2da88190b3a90f2d6db17dea completed May 22, 2026, 1:41 p.m.
NEDg Description generation batch_6a105dd29510819096f65388a14d9b77 completed May 22, 2026, 1:44 p.m.
NED2 Entity disambiguation (via description) batch_6a105e8a33d48190bbfcd28b42b6c0af completed May 22, 2026, 1:47 p.m.
Created at: April 21, 2026, 12:36 p.m.