Triple
T20358068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rémi Ochlik |
E496702
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Rémi Ochlik |
—
|
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: Rémi Ochlik | Statement: [Rémi Ochlik, name, Rémi Ochlik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rémi Ochlik Context triple: [Rémi Ochlik, name, Rémi Ochlik]
-
A.
Rémi Ochlik
chosen
Rémi Ochlik was a French photojournalist renowned for his conflict-zone reporting, who was killed while covering the Syrian civil war.
-
B.
Laurent Jouvenet
Laurent Jouvenet was a French painter of the 17th century, known as the father of the more famous Baroque artist Jean Jouvenet.
-
C.
Olivier Pomel
Olivier Pomel is a French entrepreneur best known as the co-founder and CEO of Datadog, a leading cloud monitoring and observability company.
-
D.
Peter Biziou
Peter Biziou is a British cinematographer known for his work on films such as "Bugsy Malone" and the Oscar-winning "Mississippi Burning."
-
E.
Frédéric Dambier
Frédéric Dambier is a French former competitive figure skater best known for his success in European and international competitions in the early 2000s.
- 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_69e0b4a3f7f48190b37f354574028ca6 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67855c3a88190b88839a47d01184d |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:25 a.m.