Triple
T11365033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monique Stalens |
E269180
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Monique |
E588005
|
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: Monique | Statement: [Monique Stalens, givenName, Monique]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monique Context triple: [Monique Stalens, givenName, Monique]
-
A.
Monique
chosen
Monique is the given name of the American comedian and Academy Award–winning actress Mo'Nique.
-
B.
Mónica
Mónica is the given name of Spanish singer and songwriter Mónica Naranjo, known for her powerful voice and dramatic pop music style.
-
C.
Rachel, Monique
"Rachel, Monique" is a conceptual art project by Sophie Calle centered on her mother’s life, identity, and death, combining text, photography, and installation to explore memory and loss.
-
D.
Monique Brown
Monique Brown is the widow of legendary NFL running back and actor Jim Brown and has been involved in various philanthropic and community initiatives.
-
E.
Monique Prim
Monique Prim is a film editor best known for her work on the French drama "Betty Blue."
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea4589908190948a8225768e1eec |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e55667d4908190b6290135eba41e54 |
completed | April 19, 2026, 10:25 p.m. |
Created at: April 8, 2026, 9:33 p.m.