Triple
T17340353
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bed and Board |
E421049
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Martine Barraqué |
—
|
NE NERFINISHED |
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: Martine Barraqué | Statement: [Bed and Board, editor, Martine Barraqué]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martine Barraqué Context triple: [Bed and Board, editor, Martine Barraqué]
-
A.
Martine Barraqué
chosen
Martine Barraqué is a French film editor known for her work on numerous notable films, including François Truffaut’s "The Man Who Loved Women."
-
B.
Françoise Noguès
Françoise Noguès is a French physician best known as the mother of Brigitte Macron, the First Lady of France.
-
C.
Françoise Imbert
Françoise Imbert is a French politician known for her role in centrist politics, including involvement in the creation of the Union of Democrats and Independents (UDI).
-
D.
Chantal Jouanno
Chantal Jouanno is a French politician and former minister known for her roles in center-right politics and environmental and sports policy.
-
E.
Michèle Delaunay
Michèle Delaunay is a French politician and former government minister known for her work in public health and social policy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a14ec90819098db2ac0d58a53e1 |
completed | April 19, 2026, 2:12 a.m. |
Created at: April 10, 2026, 5:44 a.m.