Triple

T24453964
Position Surface form Disambiguated ID Type / Status
Subject John IV of Armagnac E616625 entity
Predicate father P120 FINISHED
Object John III of Armagnac
John III of Armagnac was a 14th-century French nobleman who held the title of Count of Armagnac and played a role in the regional politics of southern France.
 E1643740 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: John III of Armagnac | Statement: [John IV of Armagnac, father, John III of Armagnac]
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: John III of Armagnac
Triple: [John IV of Armagnac, father, John III of Armagnac]
Generated description
John III of Armagnac was a 14th-century French nobleman who held the title of Count of Armagnac and played a role in the regional politics of southern 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_69e2d7ef9fe08190a0613908758b4e86 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f298c4f1d881909d9119fec74afe53 completed April 29, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10046304d08190be1561847970edcf completed May 22, 2026, 7:23 a.m.
NEDg Description generation batch_6a10060d1ab081909d164eaee17906dd completed May 22, 2026, 7:30 a.m.
NED2 Entity disambiguation (via description) batch_6a10068e201081909510138c6caf24fa completed May 22, 2026, 7:32 a.m.
Created at: April 18, 2026, 2:18 a.m.