Triple

T33006644
Position Surface form Disambiguated ID Type / Status
Subject Lady of Mann E844519 entity
Predicate subsequentOperator P5677 FINISHED
Object Hellenic Mediterranean Lines
Hellenic Mediterranean Lines was a Greek shipping company that operated passenger and ferry services across the Mediterranean Sea.
 E2031558 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: Hellenic Mediterranean Lines | Statement: [Lady of Mann, subsequentOperator, Hellenic Mediterranean Lines]
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: Hellenic Mediterranean Lines
Triple: [Lady of Mann, subsequentOperator, Hellenic Mediterranean Lines]
Generated description
Hellenic Mediterranean Lines was a Greek shipping company that operated passenger and ferry services across the Mediterranean Sea.

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_69f3494e59f08190b9127c693e5c7e8f completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d27b24b88190adcbd9634b1b7885 completed May 3, 2026, 4:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34dacec4b081909ff2bd7951a9c541 completed June 19, 2026, 5:59 a.m.
NEDg Description generation batch_6a34db4fc32c8190a2277b24a9031669 completed June 19, 2026, 6:01 a.m.
NED2 Entity disambiguation (via description) batch_6a34dbec0c9c8190ac5b263151322aba completed June 19, 2026, 6:04 a.m.
Created at: May 1, 2026, 1:23 a.m.