Triple
T2343975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claude Perrault |
E45088
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Claude |
E1167
|
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: Claude | Statement: [Claude Perrault, givenName, Claude]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claude Context triple: [Claude Perrault, givenName, Claude]
-
A.
Claude
chosen
Claude is a given name most famously associated with Claude Shannon, the American mathematician and electrical engineer known as the father of information theory.
-
B.
Anthropic Claude
Anthropic Claude is an advanced AI assistant developed by Anthropic, designed to provide helpful, honest, and safe natural language interactions.
-
C.
Blaise
Blaise is a given name most famously borne by the French mathematician, physicist, and philosopher Blaise Pascal.
-
D.
Michel
Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
-
E.
Claude Lancelot
Claude Lancelot was a 17th-century French grammarian and educator associated with the Port-Royal school, known for his influential works on grammar and language pedagogy.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6afe38c81909deb1de2a1c4eda9 |
completed | March 7, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae96253f0c81908b866c656c3f5bea |
completed | March 9, 2026, 9:43 a.m. |
Created at: March 4, 2026, 7:52 p.m.