Triple

T25251049
Position Surface form Disambiguated ID Type / Status
Subject Louis the Quarrelsome E632741 entity
Predicate alsoKnownAs P39 FINISHED
Object Louis I of Navarre
Louis I of Navarre, known as Louis the Quarrelsome, was a 14th-century French Capetian prince who ruled as King of Navarre and later became King Louis X of France.
E1685271 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: Louis I of Navarre | Statement: [Louis the Quarrelsome, alsoKnownAs, Louis I of Navarre]
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: Louis I of Navarre
Triple: [Louis the Quarrelsome, alsoKnownAs, Louis I of Navarre]
Generated description
Louis I of Navarre, known as Louis the Quarrelsome, was a 14th-century French Capetian prince who ruled as King of Navarre and later became King Louis X 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_69e75a8fdd3881909ba0b05aa5da92a7 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f4808b8ab08190b48cca8408c88bdf completed May 1, 2026, 10:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10ad40ebe08190881db706232dc2e8 completed May 22, 2026, 7:23 p.m.
NEDg Description generation batch_6a10ae0e67c0819087189306e39cdbc7 completed May 22, 2026, 7:27 p.m.
NED2 Entity disambiguation (via description) batch_6a10ae851d548190a19c0f9293b99e24 completed May 22, 2026, 7:29 p.m.
Created at: April 21, 2026, 1:11 p.m.