Triple
T27458427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marie Adélaïde de Bourbon |
E692664
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object |
Mademoiselle de Penthièvre
Mademoiselle de Penthièvre was a French princess of the Bourbon-Penthièvre line, granddaughter of Louis XIV’s legitimized son the Count of Toulouse and one of the wealthiest heiresses of the late Ancien Régime.
|
E1774899
|
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: Mademoiselle de Penthièvre | Statement: [Marie Adélaïde de Bourbon, title, Mademoiselle de Penthièvre]
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: Mademoiselle de Penthièvre Triple: [Marie Adélaïde de Bourbon, title, Mademoiselle de Penthièvre]
Generated description
Mademoiselle de Penthièvre was a French princess of the Bourbon-Penthièvre line, granddaughter of Louis XIV’s legitimized son the Count of Toulouse and one of the wealthiest heiresses of the late Ancien Régime.
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_69ef5207903881909427745cda05d27a |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f62dcb69848190a512b1bf1fc9a5c7 |
completed | May 2, 2026, 5 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12bbda67c0819085a85473ce39dfc1 |
completed | May 24, 2026, 8:50 a.m. |
| NEDg | Description generation | batch_6a12bc906eb481908d12f171b1230dbe |
completed | May 24, 2026, 8:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12bd38f1948190a0b1f05ff28d8289 |
completed | May 24, 2026, 8:56 a.m. |
Created at: April 27, 2026, 12:49 p.m.