Triple
T7656048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | André Tardieu |
E173384
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Tardieu
Tardieu is a French surname historically associated with several notable figures in politics, arts, and sciences.
|
E679092
|
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: Tardieu | Statement: [André Tardieu, familyName, Tardieu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tardieu Context triple: [André Tardieu, familyName, Tardieu]
-
A.
Mouton-Duvernet
Mouton-Duvernet is a Paris Métro station in the 14th arrondissement, serving the Montparnasse area and named after the French general Régis Barthélemy Mouton-Duvernet.
-
B.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
C.
Sévrier
Sévrier is a French lakeside commune in the Haute-Savoie department, known for its scenic setting on the western shore of Lake Annecy near the Alps.
-
D.
Mistinguett
Mistinguett was a famous French actress and singer of the early 20th century, celebrated as one of Paris’s most iconic music-hall stars.
-
E.
Dubouzet
Dubouzet is the namesake of Cape Dubouzet, a geographic feature likely honoring a historical figure such as an explorer, naval officer, or cartographer.
- 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: Tardieu Triple: [André Tardieu, familyName, Tardieu]
Generated description
Tardieu is a French surname historically associated with several notable figures in politics, arts, and sciences.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tardieu Target entity description: Tardieu is a French surname historically associated with several notable figures in politics, arts, and sciences.
-
A.
Mouton-Duvernet
Mouton-Duvernet is a Paris Métro station in the 14th arrondissement, serving the Montparnasse area and named after the French general Régis Barthélemy Mouton-Duvernet.
-
B.
Sauvy
Sauvy is a French surname most notably borne by Alfred Sauvy, a prominent demographer, sociologist, and economist.
-
C.
Sévrier
Sévrier is a French lakeside commune in the Haute-Savoie department, known for its scenic setting on the western shore of Lake Annecy near the Alps.
-
D.
Mistinguett
Mistinguett was a famous French actress and singer of the early 20th century, celebrated as one of Paris’s most iconic music-hall stars.
-
E.
Dubouzet
Dubouzet is the namesake of Cape Dubouzet, a geographic feature likely honoring a historical figure such as an explorer, naval officer, or cartographer.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7018ea3688190907c3ac7d25e3da6 |
completed | March 27, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b05846c8190b49540aeae43dd9a |
completed | March 29, 2026, 3:22 a.m. |
| NEDg | Description generation | batch_69c89baadac081909921bb6215e79319 |
completed | March 29, 2026, 3:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c89c698f2c8190b5d2717835bd1d82 |
completed | March 29, 2026, 3:28 a.m. |
Created at: March 27, 2026, 3:59 p.m.