Triple

T24124647
Position Surface form Disambiguated ID Type / Status
Subject Port of Cádiz E597759 entity
Predicate regulatoryFramework P1051 FINISHED
Object Spanish Ports Law
The Spanish Ports Law is a national legal framework that governs the organization, management, and operation of Spain’s state-owned ports and port authorities.
 E640935 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: Spanish Ports Law | Statement: [Port of Cádiz, regulatoryFramework, Spanish Ports 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: Spanish Ports Law
Triple: [Port of Cádiz, regulatoryFramework, Spanish Ports Law]
Generated description
The Spanish Ports Law is a national legal framework that governs the organization, management, and operation of Spain’s state-owned ports and port authorities.

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_69e288c808b881909fed7d18f04bcbbe completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1dee7e7a4819094feb5da7050745f completed April 29, 2026, 10:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fad19ed148190941802509c24b5b2 completed May 22, 2026, 1:10 a.m.
NEDg Description generation batch_6a0fb121d0dc81909c74c152c17b5d56 completed May 22, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a0fb1d7653c81909d8903f88d91a085 completed May 22, 2026, 1:31 a.m.
Created at: April 17, 2026, 11:06 p.m.