Triple
T12552230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judith Collins |
E300124
|
entity |
| Predicate | representedElectorate |
P15002
|
FINISHED |
| Object |
Papakura
Papakura is a New Zealand parliamentary electorate in South Auckland, known for its mix of urban and semi-rural communities.
|
E1230392
|
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: Papakura | Statement: [Judith Collins, representedElectorate, Papakura]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Papakura Context triple: [Judith Collins, representedElectorate, Papakura]
-
A.
Kasukabe
Kasukabe is a city in Japan known for its suburban character within the Greater Tokyo area and as the setting of the popular manga and anime series "Crayon Shin-chan."
-
B.
Kiga
Kiga is a Bantu language spoken primarily by the Bakiga people of southwestern Uganda, near the Great Lakes region of East Africa.
-
C.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
-
D.
Urakawa
Urakawa is a coastal town in Hokkaido, Japan, known for its horse breeding industry and scenic Pacific shoreline.
-
E.
Tomonoura
Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
- 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: Papakura Triple: [Judith Collins, representedElectorate, Papakura]
Generated description
Papakura is a New Zealand parliamentary electorate in South Auckland, known for its mix of urban and semi-rural communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Papakura Target entity description: Papakura is a New Zealand parliamentary electorate in South Auckland, known for its mix of urban and semi-rural communities.
-
A.
Kasukabe
Kasukabe is a city in Japan known for its suburban character within the Greater Tokyo area and as the setting of the popular manga and anime series "Crayon Shin-chan."
-
B.
Kiga
Kiga is a Bantu language spoken primarily by the Bakiga people of southwestern Uganda, near the Great Lakes region of East Africa.
-
C.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
-
D.
Urakawa
Urakawa is a coastal town in Hokkaido, Japan, known for its horse breeding industry and scenic Pacific shoreline.
-
E.
Tomonoura
Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
- 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_69d6ada707008190aaec1238117c9379 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9548444d081908f00cea1ce7032c7 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a009d20a42c819090319629544fa349 |
completed | May 10, 2026, 2:58 p.m. |
| NEDg | Description generation | batch_6a009e48cde08190aa7b569280a59d0e |
completed | May 10, 2026, 3:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a009ebf71608190bc1b3c063372d21b |
completed | May 10, 2026, 3:05 p.m. |
Created at: April 8, 2026, 9:58 p.m.