Triple
T26915274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Pléiade |
E677500
|
entity |
| Predicate | member |
P10
|
FINISHED |
| Object |
Pontus de Tyard
Pontus de Tyard was a 16th-century French poet, humanist, and cleric associated with the Renaissance literary movement La Pléiade.
|
E1746273
|
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: Pontus de Tyard | Statement: [La Pléiade, member, Pontus de Tyard]
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: Pontus de Tyard Triple: [La Pléiade, member, Pontus de Tyard]
Generated description
Pontus de Tyard was a 16th-century French poet, humanist, and cleric associated with the Renaissance literary movement La Pléiade.
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_69eee9bcef1c8190be88586bb902bb9b |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61fdd39ec8190aaf1714330459136 |
completed | May 2, 2026, 4:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a121eb4f5208190939fe86ba18a47ab |
completed | May 23, 2026, 9:40 p.m. |
| NEDg | Description generation | batch_6a121f5c372481909cfd4c5ebc39f5ea |
completed | May 23, 2026, 9:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a122004aadc819084dbaa834408a0bc |
completed | May 23, 2026, 9:45 p.m. |
Created at: April 27, 2026, 6:04 a.m.