Triple

T37845257
Position Surface form Disambiguated ID Type / Status
Subject William Blakeney E943582 entity
Predicate positionHeld P8 FINISHED
Object Lieutenant Governor of Minorca
The Lieutenant Governor of Minorca was a senior British colonial official responsible for the island’s military defense and civil administration during periods of British rule.
E2245243 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: Lieutenant Governor of Minorca | Statement: [William Blakeney, positionHeld, Lieutenant Governor of Minorca]
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: Lieutenant Governor of Minorca
Triple: [William Blakeney, positionHeld, Lieutenant Governor of Minorca]
Generated description
The Lieutenant Governor of Minorca was a senior British colonial official responsible for the island’s military defense and civil administration during periods of British rule.

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_69f76eeb0f7081908d6d3adbc469889c completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbb21ead6481908766ca90a676c331 completed May 6, 2026, 9:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40fb8daab081908a5a1756b427c18c completed June 28, 2026, 10:46 a.m.
NEDg Description generation batch_6a40fc9d8174819082a96a1d282347e0 completed June 28, 2026, 10:51 a.m.
NED2 Entity disambiguation (via description) batch_6a40fd33f74081908f097d1ec2685794 completed June 28, 2026, 10:53 a.m.
Created at: May 3, 2026, 4:19 p.m.