Triple

T27160727
Position Surface form Disambiguated ID Type / Status
Subject Aveline de Clare E682652 entity
Predicate spouseNobleRank P17687 FINISHED
Object 2nd Earl of Essex
The 2nd Earl of Essex was an English nobleman of the High Middle Ages, a prominent magnate and landholder closely connected to the royal court.
 E1766633 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: 2nd Earl of Essex | Statement: [Aveline de Clare, spouseNobleRank, 2nd Earl of Essex]
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: 2nd Earl of Essex
Triple: [Aveline de Clare, spouseNobleRank, 2nd Earl of Essex]
Generated description
The 2nd Earl of Essex was an English nobleman of the High Middle Ages, a prominent magnate and landholder closely connected to the royal court.

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_69eefacf6e788190a75a64399d9e3109 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f62509f5ec8190a6bc6ca211a0f6a1 completed May 2, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129c9249848190a2a170bffe28f501 completed May 24, 2026, 6:37 a.m.
NEDg Description generation batch_6a129e71ddc481908a3659f89b0beb0f completed May 24, 2026, 6:45 a.m.
NED2 Entity disambiguation (via description) batch_6a129fa2db9c8190be46762dee9a6394 completed May 24, 2026, 6:50 a.m.
Created at: April 27, 2026, 9:18 a.m.