Triple
T8958153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armand Gensonné |
E213528
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Armand |
E351737
|
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: Armand | Statement: [Armand Gensonné, givenName, Armand]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Armand Context triple: [Armand Gensonné, givenName, Armand]
-
A.
Armand
Armand is the given name of Cardinal Richelieu, the powerful 17th-century French statesman and chief minister to King Louis XIII.
-
B.
Armand
Armand is the given first name of the French poet and Nobel laureate Sully Prudhomme.
-
C.
Armand
chosen
Armand is a masculine given name of French origin, historically associated with nobility and used in various European and English-speaking cultures.
-
D.
Armand
Armand is the given first name of the 19th-century French physicist Hippolyte Fizeau, known for his pioneering measurements of the speed of light.
-
E.
Armand Dorian
Armand Dorian is an American trauma surgeon and television personality best known for serving as the medical expert on the TV series "Deadliest Warrior."
- 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_69ca8399ad2081909f8fa41d4314c215 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6729ab7c8190a6168f0aa70a5520 |
completed | April 1, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc9430a8c819096e0df38c107d1bd |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 7 p.m.