Triple
T13032198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tarita Teriipaia |
E326467
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Tarita
Tarita is a Tahitian actress best known for her role in the film "Mutiny on the Bounty" and for being the third wife of actor Marlon Brando.
|
E1017109
|
NE FINISHED |
How this triple was built (4 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: Tarita | Statement: [Tarita Teriipaia, givenName, Tarita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarita Context triple: [Tarita Teriipaia, givenName, Tarita]
-
A.
Ritva
Ritva, also known as Rabbi Yom Tov ben Avraham Ishbili, was a prominent 13th–14th century Spanish Talmudic commentator and halakhic authority whose works are central in traditional Jewish scholarship.
-
B.
Kataja
Kataja is a short form or nickname of the female given name Katarina.
-
C.
Taipale
Taipale is a locality in Finland historically notable as a major battleground during the Winter War between Finland and the Soviet Union.
-
D.
Rantala
Rantala is a Finnish surname borne by various notable individuals in fields such as politics, sports, and the arts.
-
E.
Keilaniemi
Keilaniemi is a coastal district in Espoo, Finland, known as a major business hub hosting numerous corporate headquarters and high-rise office buildings.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tarita Triple: [Tarita Teriipaia, givenName, Tarita]
Generated description
Tarita is a Tahitian actress best known for her role in the film "Mutiny on the Bounty" and for being the third wife of actor Marlon Brando.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tarita Target entity description: Tarita is a Tahitian actress best known for her role in the film "Mutiny on the Bounty" and for being the third wife of actor Marlon Brando.
-
A.
Ritva
Ritva, also known as Rabbi Yom Tov ben Avraham Ishbili, was a prominent 13th–14th century Spanish Talmudic commentator and halakhic authority whose works are central in traditional Jewish scholarship.
-
B.
Kataja
Kataja is a short form or nickname of the female given name Katarina.
-
C.
Taipale
Taipale is a locality in Finland historically notable as a major battleground during the Winter War between Finland and the Soviet Union.
-
D.
Rantala
Rantala is a Finnish surname borne by various notable individuals in fields such as politics, sports, and the arts.
-
E.
Keilaniemi
Keilaniemi is a coastal district in Espoo, Finland, known as a major business hub hosting numerous corporate headquarters and high-rise office buildings.
- F. None of above. chosen
Provenance (5 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97efe72348190b52fb4068f5fb829 |
completed | April 10, 2026, 10:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6cbcd25108190a6c4a129cde81534 |
completed | May 3, 2026, 4:15 a.m. |
| NEDg | Description generation | batch_69f6cd0d21e08190855dcbee000fc25d |
completed | May 3, 2026, 4:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6ce6b220c8190b1f49a9b2bfce692 |
completed | May 3, 2026, 4:26 a.m. |
Created at: April 9, 2026, 8:54 p.m.