Triple
T21713729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-François Revel |
E535968
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | L’Express |
—
|
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: L’Express | Statement: [Jean-François Revel, employer, L’Express]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L’Express Context triple: [Jean-François Revel, employer, L’Express]
-
A.
L’Express
chosen
L’Express is a major French weekly news magazine known for its political and intellectual commentary.
-
B.
The Express
The Express is a sports drama film that tells the inspiring true story of Ernie Davis, the first African American to win the Heisman Trophy.
-
C.
Sunday Express
Sunday Express is a British national Sunday newspaper known for its conservative-leaning coverage of news, politics, and entertainment.
-
D.
Expressen
Expressen is a major Swedish daily tabloid newspaper known for its national coverage, opinion pieces, and investigative journalism.
-
E.
The Times
The Times is a long-established and influential British daily newspaper known for its national and international news coverage, commentary, and analysis.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efb5369be88190bafc10863d4d1bd7 |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 16, 2026, 6:47 p.m.