Triple
T10254906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Zealand film industry |
E240436
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object |
Merata Mita
Merata Mita was a pioneering Māori filmmaker and documentarian from New Zealand, renowned for her politically charged works and advocacy for Indigenous storytelling in cinema.
|
E850933
|
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: Merata Mita | Statement: [New Zealand film industry, associatedWith, Merata Mita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Merata Mita Context triple: [New Zealand film industry, associatedWith, Merata Mita]
-
A.
Mikita
Mikita is a given name and surname of Slavic origin used by various notable individuals.
-
B.
Taku Matoba
Taku Matoba is a video game programmer known for his work on Nintendo's acclaimed title Super Mario Galaxy.
-
C.
Ranu Kumbolo
Ranu Kumbolo is a scenic high-altitude lake in East Java, Indonesia, popular as a rest and camping spot for hikers on the route to Mount Semeru.
-
D.
Nakasero
Nakasero is a central and upscale neighborhood in Kampala, Uganda, known for its government offices, embassies, hotels, and commercial centers.
-
E.
Tanimaiaki
Tanimaiaki is a settlement on the atoll of Abemama in the island nation of Kiribati in the central Pacific Ocean.
- 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: Merata Mita Triple: [New Zealand film industry, associatedWith, Merata Mita]
Generated description
Merata Mita was a pioneering Māori filmmaker and documentarian from New Zealand, renowned for her politically charged works and advocacy for Indigenous storytelling in cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Merata Mita Target entity description: Merata Mita was a pioneering Māori filmmaker and documentarian from New Zealand, renowned for her politically charged works and advocacy for Indigenous storytelling in cinema.
-
A.
Mikita
Mikita is a given name and surname of Slavic origin used by various notable individuals.
-
B.
Taku Matoba
Taku Matoba is a video game programmer known for his work on Nintendo's acclaimed title Super Mario Galaxy.
-
C.
Ranu Kumbolo
Ranu Kumbolo is a scenic high-altitude lake in East Java, Indonesia, popular as a rest and camping spot for hikers on the route to Mount Semeru.
-
D.
Nakasero
Nakasero is a central and upscale neighborhood in Kampala, Uganda, known for its government offices, embassies, hotels, and commercial centers.
-
E.
Tanimaiaki
Tanimaiaki is a settlement on the atoll of Abemama in the island nation of Kiribati in the central Pacific Ocean.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d24b9a308190bba6d8e3e22e5ee0 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7cec62c819083e493e0fc7c65b5 |
completed | April 9, 2026, 12:50 a.m. |
| NEDg | Description generation | batch_69d6fa3149e48190825600ee28ed7231 |
completed | April 9, 2026, 1 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fcbee9088190869b1fcb6f909be3 |
completed | April 9, 2026, 1:11 a.m. |
Created at: April 6, 2026, 11:30 a.m.