Triple
T4390523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marielle Heller |
E99349
|
entity |
| Predicate | givenName |
P17
|
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: [Marielle Heller, givenName, Marielle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marielle Context triple: [Marielle Heller, givenName, 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.
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.
-
D.
Nakia
Nakia is a skilled Wakandan spy and warrior in the Marvel Cinematic Universe, known for her courage, compassion, and close ties to T’Challa and Wakanda.
-
E.
Shosanna Dreyfus
Shosanna Dreyfus is a vengeful French Jewish cinema owner in Quentin Tarantino’s film "Inglourious Basterds," known for orchestrating a plot to destroy high-ranking Nazis.
- 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_69b3454f739481909ff6c28331f0c0b9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352843d7c8190929b94c94eaa63df |
completed | March 12, 2026, 11:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e530428881908d125971263bd747 |
completed | March 14, 2026, 10:46 p.m. |
Created at: March 12, 2026, 11:19 p.m.