Triple
T13353824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duke of Tallard |
E318135
|
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: [Duke of Tallard, givenName, Camille]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camille Context triple: [Duke of Tallard, givenName, Camille]
-
A.
Camille
Camille is a classic 1936 romantic drama film starring Greta Garbo as a tragic Parisian courtesan.
-
B.
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.
-
C.
Camille
Camille is a character in Tennessee Williams’ play "Camino Real," a dreamlike drama set in a surreal, decaying town.
-
D.
Camille Roux
Camille Roux was an artist associated with the Impressionist movement who participated in the historic Impressionist exhibitions in late 19th-century France.
-
E.
Camille Henry
Camille Henry was a skilled Canadian ice hockey center best known for his prolific scoring with the New York Rangers in the 1950s and 1960s.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8d520881908aa23c7102b72b72 |
completed | April 11, 2026, 1:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f49e5548190b14d09daea628e6b |
completed | May 3, 2026, 10:11 a.m. |
Created at: April 9, 2026, 9:32 p.m.