Triple
T220268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | René Cassin |
E4197
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
René
René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
|
E30626
|
NE FINISHED |
How this triple was built (4 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: René | Statement: [René Cassin, givenName, René]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: René Context triple: [René Cassin, givenName, René]
-
A.
Pierre
Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
-
B.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
C.
Georges
Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
-
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.
André
André is a given name of French origin commonly used in various languages as a form of "Andrew."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: René Triple: [René Cassin, givenName, René]
Generated description
René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: René Target entity description: René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
-
A.
Pierre
Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
-
B.
Jacques
Jacques is the French form of the given name James, commonly used in French-speaking countries.
-
C.
Georges
Georges is a masculine given name of Greek origin, commonly used in French-speaking countries and derived from the name George, meaning "farmer" or "earthworker."
-
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.
André
André is a given name of French origin commonly used in various languages as a form of "Andrew."
- F. None of above. chosen
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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c6d0fa08190810139b14f4851bc |
completed | Feb. 28, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a36730f0a88190aac3d2e796ee544f |
completed | Feb. 28, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69a3678a8c988190895796b2e3de3021 |
completed | Feb. 28, 2026, 10:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3687171208190b470610bf2a2268e |
completed | Feb. 28, 2026, 10:13 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.