Triple

T25366849
Position Surface form Disambiguated ID Type / Status
Subject Port of Cayenne E632822 entity
Predicate regulationJurisdiction P7728 FINISHED
Object French maritime law
French maritime law is the body of legal rules and regulations governing shipping, navigation, maritime commerce, and related activities under the jurisdiction of France.
E142342 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: French maritime law | Statement: [Port of Cayenne, regulationJurisdiction, French maritime law]
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: French maritime law
Triple: [Port of Cayenne, regulationJurisdiction, French maritime law]
Generated description
French maritime law is the body of legal rules and regulations governing shipping, navigation, maritime commerce, and related activities under the jurisdiction of France.

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_69e75a90c0dc819092f928b6ea0ecc72 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f4a10eb1748190aa576850282c808d completed May 1, 2026, 12:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a107605ca008190881b887efef07810 completed May 22, 2026, 3:28 p.m.
NEDg Description generation batch_6a10771a5a648190844a509e6ac507be completed May 22, 2026, 3:32 p.m.
NED2 Entity disambiguation (via description) batch_6a1077b79abc819099f92e2e2cc19c5d completed May 22, 2026, 3:35 p.m.
Created at: April 21, 2026, 1:37 p.m.