Triple
T1332921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sukarno |
E28683
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Hartini
Hartini was the second wife of Indonesia’s first president, Sukarno, and a notable figure in mid-20th-century Indonesian social and political life.
|
E151090
|
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: Hartini | Statement: [Sukarno, spouse, Hartini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hartini Context triple: [Sukarno, spouse, Hartini]
-
A.
Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
-
B.
Hana
Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
-
C.
Roro
Roro is the ISO 15924 script code assigned to the undeciphered Rongorongo script of Easter Island.
-
D.
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.
-
E.
Lolo Soetoro
Lolo Soetoro was an Indonesian geographer and government official best known as the stepfather of U.S. President Barack Obama.
- 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: Hartini Triple: [Sukarno, spouse, Hartini]
Generated description
Hartini was the second wife of Indonesia’s first president, Sukarno, and a notable figure in mid-20th-century Indonesian social and political life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hartini Target entity description: Hartini was the second wife of Indonesia’s first president, Sukarno, and a notable figure in mid-20th-century Indonesian social and political life.
-
A.
Hana
Hana is a small, remote town on the eastern coast of Maui, Hawaii, known for its lush landscapes, waterfalls, and the scenic Road to Hana.
-
B.
Hana
Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during World War II.
-
C.
Roro
Roro is the ISO 15924 script code assigned to the undeciphered Rongorongo script of Easter Island.
-
D.
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.
-
E.
Lolo Soetoro
Lolo Soetoro was an Indonesian geographer and government official best known as the stepfather of U.S. President Barack Obama.
- 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1e7f1388190a6e4eb65a7997380 |
completed | March 1, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbf383b24819092acd076130ca5c0 |
completed | March 8, 2026, 12:13 a.m. |
| NEDg | Description generation | batch_69acbf77a3748190a510ea10d8ae4373 |
completed | March 8, 2026, 12:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acbfe5eae88190ba65808402ada37f |
completed | March 8, 2026, 12:16 a.m. |
Created at: March 1, 2026, 7:55 p.m.