Triple
T1797771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camille Pissarro |
E39643
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Camille |
E114928
|
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: Camille | Statement: [Camille Pissarro, givenName, Camille]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camille Context triple: [Camille Pissarro, givenName, Camille]
-
A.
Camille
chosen
Camille is a French given name used for both males and females, historically associated with figures such as the revolutionary journalist Camille Desmoulins.
-
B.
Marguerite
Marguerite is a French given name, equivalent to Margaret, commonly used for women and also meaning "daisy" in French.
-
C.
Jeanne
Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
-
D.
Delilah
Delilah is a biblical figure best known for betraying Samson by discovering and revealing the secret of his strength.
-
E.
Marguerite De La Motte
Marguerite De La Motte was an American silent film actress best known for her leading roles in early 1920s adventure and drama films.
- 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_69a88632aa588190ba3978fde0db5bbd |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa6566cd808190a67f33ad47d8ca8e |
completed | March 6, 2026, 5:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adb5d6cee88190b26814134037aba6 |
completed | March 8, 2026, 5:45 p.m. |
Created at: March 4, 2026, 7:32 p.m.