Triple
T13313051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean-Pierre Marielle |
E317119
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Marielle |
E317119
|
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: Marielle | Statement: [Jean-Pierre Marielle, familyName, Marielle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marielle Context triple: [Jean-Pierre Marielle, familyName, Marielle]
-
A.
Marielle
chosen
Marielle is a French surname most notably borne by the acclaimed actor Jean-Pierre Marielle.
-
B.
Marielle Scott
Marielle Scott is an American actress known for her work in film and television, including roles in projects like the miniseries "A Teacher."
-
C.
Myrah
Myrah is a feminine given name, typically considered a modern or stylistic variant of the name Myra.
-
D.
Mariann
Mariann is the given name of Mariann Edgar Budde, an American Episcopal bishop known for her leadership in the Episcopal Diocese of Washington.
-
E.
Karla
Karla is the elusive Soviet spymaster and primary antagonist of John le Carré’s George Smiley novels, symbolizing the Cold War espionage rivalry between British intelligence and the KGB.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990f6d34c8190ba19dc2df7d42c22 |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716e7b9a48190a33b04df8ad45ed8 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:29 p.m.