Triple
T17385977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claude Crépeau |
E422687
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Claude |
—
|
NE NERFINISHED |
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 Crépeau, givenName, Claude]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claude Context triple: [Claude Crépeau, 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.
Claude
Claude is the NATO reporting name for the Mitsubishi A5M, a Japanese carrier-based fighter aircraft used primarily in the late 1930s and early World War II.
-
C.
Anthropic Claude
Anthropic Claude is an advanced AI assistant developed by Anthropic, designed to provide helpful, honest, and safe natural language interactions.
-
D.
Claude Jade
Claude Jade was a French actress best known for her role as Christine in François Truffaut’s Antoine Doinel film series.
-
E.
Ray Tune
Ray Tune is a scalable hyperparameter tuning and experiment management library for machine learning, built on the Ray distributed computing framework.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d710288190bf0f4762801fefae |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a89c5008190a277a68e5cfe67b7 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:45 a.m.