Triple
T5564297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parler tout bas |
E145842
|
entity |
| Predicate | follows |
P134
|
FINISHED |
| Object | L'Alizé |
E532585
|
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: L'Alizé | Statement: [Parler tout bas, follows, L'Alizé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L'Alizé Context triple: [Parler tout bas, follows, L'Alizé]
-
A.
L'Alizé
chosen
L'Alizé is a 2000 French pop song by singer Alizée that became a major hit across Europe and helped launch her international career.
-
B.
L’Espoir
L’Espoir is a 1937 novel by André Malraux that portrays the political and human drama of the Spanish Civil War.
-
C.
Palmesana
Palmesana is the term used to refer to a female inhabitant or native of Palma de Mallorca, a city on the Spanish island of Mallorca.
-
D.
Renault Super Goélette
The Renault Super Goélette was a light commercial van and truck produced by the French manufacturer Renault, widely used in the 1960s and 1970s for utility and transport purposes.
-
E.
Gourette
Gourette is a French mountain ski resort village in the Pyrenees, known for its alpine slopes and scenic high-altitude setting.
- 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_69c008fdae24819081aa002ad99cd966 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02032330c819094f2bc1e8c93a5b6 |
completed | March 22, 2026, 5 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c097bd26e08190a687a08323f1400a |
completed | March 23, 2026, 1:30 a.m. |
Created at: March 22, 2026, 3:36 p.m.