Triple
T13686369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Claude Cohen-Tannoudji |
E328138
|
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 Cohen-Tannoudji, givenName, Claude]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claude Context triple: [Claude Cohen-Tannoudji, 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.
Ray Tune
Ray Tune is a scalable hyperparameter tuning and experiment management library for machine learning, built on the Ray distributed computing framework.
-
E.
Claudy
Claudy is a small village and townland in County Londonderry, Northern Ireland, situated near the River Faughan and known for its rural setting and local community.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc670968881908e2b4fdf656c7285 |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7944981ec8190be5ff39b7c2c70ab |
completed | May 3, 2026, 6:30 p.m. |
Created at: April 9, 2026, 9:53 p.m.