Triple

T37704598
Position Surface form Disambiguated ID Type / Status
Subject John of St. Thomas E939165 entity
Predicate birthName P65 FINISHED
Object João Poinsot
João Poinsot was a 17th-century Portuguese Dominican philosopher and theologian, better known in Latin as John of St. Thomas, noted for his influential work in Thomistic metaphysics and logic.
E2242874 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: João Poinsot | Statement: [John of St. Thomas, birthName, João Poinsot]
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: João Poinsot
Triple: [John of St. Thomas, birthName, João Poinsot]
Generated description
João Poinsot was a 17th-century Portuguese Dominican philosopher and theologian, better known in Latin as John of St. Thomas, noted for his influential work in Thomistic metaphysics and logic.

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_69f76edb49dc8190b951dce9ce6ef789 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fbae445df0819098c9b8af650ffcbd completed May 6, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40e06f9e2881908550fb3df6980002 completed June 28, 2026, 8:50 a.m.
NEDg Description generation batch_6a40e23df7348190b2cb9ec55e766735 completed June 28, 2026, 8:58 a.m.
NED2 Entity disambiguation (via description) batch_6a40ed48f9e08190b741f8aac12b70ed completed June 28, 2026, 9:45 a.m.
Created at: May 3, 2026, 4:18 p.m.