Triple
T16954760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hubert Robert |
E411269
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hubert |
E164283
|
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: Hubert | Statement: [Hubert Robert, givenName, Hubert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hubert Context triple: [Hubert Robert, givenName, Hubert]
-
A.
Hubert
chosen
Hubert is a masculine given name of Germanic origin meaning "bright heart" or "shining intellect," historically borne by saints, nobles, and notable public figures.
-
B.
Roger Hubert
Roger Hubert was a French cinematographer known for his work on mid-20th-century films, contributing to the visual style of classic French cinema.
-
C.
Huberte Rupert
Huberte Rupert is a member of South Africa’s prominent Rupert family, known for its extensive business and philanthropic influence.
-
D.
Thomas Hubert
Thomas Hubert is an author known for his work on the artificial intelligence program AlphaGo Zero.
-
E.
Hubert Hawkins
Hubert Hawkins is the bumbling yet brave entertainer-turned-hero portrayed by Danny Kaye in the 1955 musical comedy film "The Court Jester."
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d01b04e88190a72735541b3bb117 |
completed | April 18, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d464762c8190a734ffdd83633f70 |
completed | May 10, 2026, 6:54 p.m. |
Created at: April 10, 2026, 5:31 a.m.