Triple
T12580286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rémy Cabella |
E300316
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rémy |
E300316
|
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: Rémy | Statement: [Rémy Cabella, givenName, Rémy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rémy Context triple: [Rémy Cabella, givenName, Rémy]
-
A.
Louiguy
Louiguy was a French composer best known for co-writing the iconic chanson "La Vie en rose," popularized by Édith Piaf.
-
B.
Remi Aubuchon
Remi Aubuchon is an American television writer and producer known for his work on science fiction and drama series.
-
C.
Rémy Cabella
chosen
Rémy Cabella is a French professional footballer known as an attacking midfielder who has played in Ligue 1 and abroad, earning recognition for his creativity and technical skill.
-
D.
Remy
Remy is a fictional character portrayed as a family member of Brian Dennehy’s character Django.
-
E.
Remy
Remy is the ambitious, food-loving rat and main protagonist of Pixar’s animated film "Ratatouille," known for his exceptional culinary talent and dream of becoming a chef in Paris.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954b867dc8190af8a70f797e4d133 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6559ba5108190b85be540a405eec8 |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 9, 2026, 5:01 p.m.