Triple
T8850501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Museum of Nature and Science |
E210625
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
Kahaku
Kahaku is Japan’s National Museum of Nature and Science in Tokyo, renowned for its extensive natural history and scientific collections and exhibitions.
|
E761275
|
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: Kahaku | Statement: [National Museum of Nature and Science, abbreviation, Kahaku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kahaku Context triple: [National Museum of Nature and Science, abbreviation, Kahaku]
-
A.
Sikka
Sikka is an Austronesian language spoken primarily by the Sikka people on the island of Flores in eastern Indonesia.
-
B.
Kaka’i
Kaka’i is a Kurdish religious minority community associated with the syncretic Ahl-e Haqq (Yarsan) faith, primarily found in parts of Iraq and Iran.
-
C.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
D.
Tahkuna
Tahkuna is a coastal settlement in northern Estonia, located on Hiiumaa Island and known for its proximity to the historic Tahkuna Lighthouse.
-
E.
Kodama
Kodama is a Japanese Shinkansen train service known for its all-stop, slower-speed runs along high-speed rail lines such as the Tokaido Shinkansen.
- 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: Kahaku Triple: [National Museum of Nature and Science, abbreviation, Kahaku]
Generated description
Kahaku is Japan’s National Museum of Nature and Science in Tokyo, renowned for its extensive natural history and scientific collections and exhibitions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kahaku Target entity description: Kahaku is Japan’s National Museum of Nature and Science in Tokyo, renowned for its extensive natural history and scientific collections and exhibitions.
-
A.
Sikka
Sikka is an Austronesian language spoken primarily by the Sikka people on the island of Flores in eastern Indonesia.
-
B.
Kaka’i
Kaka’i is a Kurdish religious minority community associated with the syncretic Ahl-e Haqq (Yarsan) faith, primarily found in parts of Iraq and Iran.
-
C.
Kogarah
Kogarah is a suburb in southern Sydney, New South Wales, Australia, known as a residential and commercial hub in the St George area.
-
D.
Tahkuna
Tahkuna is a coastal settlement in northern Estonia, located on Hiiumaa Island and known for its proximity to the historic Tahkuna Lighthouse.
-
E.
Kodama
Kodama is a Japanese Shinkansen train service known for its all-stop, slower-speed runs along high-speed rail lines such as the Tokaido Shinkansen.
- 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_69ca838a424c8190b1ecac115c2927e7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc60c2300c819097b1ca6ebe2f749a |
completed | April 1, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf89cb853c8190a7664f2e7de0de87 |
completed | April 3, 2026, 9:35 a.m. |
| NEDg | Description generation | batch_69cf8ab7da348190b423f0768fe9dc1a |
completed | April 3, 2026, 9:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf8bd252a4819098891bbb67baf897 |
completed | April 3, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:49 p.m.