Triple
T719739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexandre Colonna-Walewski |
E14389
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Alexandre |
E42018
|
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: Alexandre | Statement: [Alexandre Colonna-Walewski, givenName, Alexandre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alexandre Context triple: [Alexandre Colonna-Walewski, givenName, Alexandre]
-
A.
Alexandre
chosen
Alexandre is a given name of Greek origin, commonly used in French and Portuguese-speaking countries as a form of Alexander.
-
B.
Édouard
Édouard is the French form of the given name Edward, commonly used in French-speaking countries.
-
C.
François
François is the given name of the French poet and essayist Sully Prudhomme, the first recipient of the Nobel Prize in Literature.
-
D.
Alexandre de Beauharnais
Alexandre de Beauharnais was a French aristocrat, general, and politician of the Revolutionary era who became the first husband of Joséphine, later Empress of France.
-
E.
Jean-Baptiste
Jean-Baptiste is the French given name of the mathematician and physicist Jean-Baptiste Joseph Fourier, known for pioneering Fourier analysis and the study of heat conduction.
- 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a58e65e8819098cba7e6a20d8f33 |
completed | March 1, 2026, 8:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d71a25c81908de9b9e59affb79f |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:37 p.m.