Triple
T672089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cardinal Richelieu |
E12992
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jean |
E57083
|
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: Jean | Statement: [Cardinal Richelieu, givenName, Jean]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jean Context triple: [Cardinal Richelieu, givenName, Jean]
-
A.
Jean
chosen
Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
-
B.
Alexis
Alexis is a given name most famously borne by the French political thinker and historian Alexis de Tocqueville.
-
C.
Marie
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
-
D.
Théodore
Théodore is a masculine given name of Greek origin, commonly used in French-speaking countries and borne by notable figures such as the Reformation theologian Théodore Beza.
-
E.
Jane
Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a021b908819086f7cfe65def4728 |
completed | March 1, 2026, 8:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d6cdc9c81909eb3240c00fa530d |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:36 p.m.