Triple

T25504496
Position Surface form Disambiguated ID Type / Status
Subject Pendik Ferry Terminal E639210 entity
Predicate isPartOfTransportNetwork P8428 FINISHED
Object Marmara maritime network
The Marmara maritime network is a regional system of ferry and sea transport routes connecting ports and terminals around the Sea of Marmara in northwestern Turkey.
E1684792 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: Marmara maritime network | Statement: [Pendik Ferry Terminal, isPartOfTransportNetwork, Marmara maritime network]
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: Marmara maritime network
Triple: [Pendik Ferry Terminal, isPartOfTransportNetwork, Marmara maritime network]
Generated description
The Marmara maritime network is a regional system of ferry and sea transport routes connecting ports and terminals around the Sea of Marmara in northwestern Turkey.

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_69e75dbd09308190b6b5f0afdc12ec6d completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f804c9f48190be4e560a60ee6242 completed May 2, 2026, 1:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ad789e3c819091e30a52edbe72ac completed May 22, 2026, 7:24 p.m.
NEDg Description generation batch_6a10af0199d48190a458303da4d3f51b completed May 22, 2026, 7:31 p.m.
NED2 Entity disambiguation (via description) batch_6a10af914a4481909fad4723d5975df5 completed May 22, 2026, 7:33 p.m.
Created at: April 21, 2026, 2:46 p.m.