Triple

T34719656
Position Surface form Disambiguated ID Type / Status
Subject Didim District E1000875 entity
Predicate locatedOnPeninsula P10961 FINISHED
Object Didim Peninsula
The Didim Peninsula is a coastal landform in southwestern Turkey known for its Aegean beaches, resort towns, and proximity to ancient sites such as the Temple of Apollo.
E2109728 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: Didim Peninsula | Statement: [Didim District, locatedOnPeninsula, Didim Peninsula]
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: Didim Peninsula
Triple: [Didim District, locatedOnPeninsula, Didim Peninsula]
Generated description
The Didim Peninsula is a coastal landform in southwestern Turkey known for its Aegean beaches, resort towns, and proximity to ancient sites such as the Temple of Apollo.

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_69f76dad3f108190a280fd0a2f4ee89a completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f7799156408190bd87449e7a1ee3fb completed May 3, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a375be48498819080b0c42330e2f6d6 completed June 21, 2026, 3:35 a.m.
NEDg Description generation batch_6a375cce6a748190989f2fffd5341e3c completed June 21, 2026, 3:38 a.m.
NED2 Entity disambiguation (via description) batch_6a375d9623888190b8766e4f1a5bd898 completed June 21, 2026, 3:42 a.m.
Created at: May 3, 2026, 3:59 p.m.