Triple
T17709106
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Speaker of the Parliament of Tuvalu |
E441513
|
entity |
| Predicate | officeHoldersInclude |
P537
|
FINISHED |
| Object |
Samuelu Teo
Samuelu Teo is a Tuvaluan politician who has served as Speaker of the Parliament of Tuvalu.
|
E1284010
|
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: Samuelu Teo | Statement: [Speaker of the Parliament of Tuvalu, officeHoldersInclude, Samuelu Teo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samuelu Teo Context triple: [Speaker of the Parliament of Tuvalu, officeHoldersInclude, Samuelu Teo]
-
A.
Lai Teck
Lai Teck was the elusive and controversial secretary-general of the Malayan Communist Party before and during World War II, later exposed as a triple agent whose betrayal crippled the communist movement in Malaya.
-
B.
Ho Iat Seng
Ho Iat Seng is a Macanese politician who has served as the Chief Executive of the Macao Special Administrative Region of China.
-
C.
Chan Sek Keong
Chan Sek Keong is a prominent Singaporean jurist who served as the country’s third Chief Justice and played a key role in shaping its modern legal system.
-
D.
Ken Seng
Ken Seng is a cinematographer known for his visually distinctive work on films such as "Obsessed."
-
E.
Soo Beng Kiang
Soo Beng Kiang is a former Malaysian badminton player best known as a top men's doubles specialist in the 1990s.
- 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: Samuelu Teo Triple: [Speaker of the Parliament of Tuvalu, officeHoldersInclude, Samuelu Teo]
Generated description
Samuelu Teo is a Tuvaluan politician who has served as Speaker of the Parliament of Tuvalu.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Samuelu Teo Target entity description: Samuelu Teo is a Tuvaluan politician who has served as Speaker of the Parliament of Tuvalu.
-
A.
Lai Teck
Lai Teck was the elusive and controversial secretary-general of the Malayan Communist Party before and during World War II, later exposed as a triple agent whose betrayal crippled the communist movement in Malaya.
-
B.
Ho Iat Seng
Ho Iat Seng is a Macanese politician who has served as the Chief Executive of the Macao Special Administrative Region of China.
-
C.
Chan Sek Keong
Chan Sek Keong is a prominent Singaporean jurist who served as the country’s third Chief Justice and played a key role in shaping its modern legal system.
-
D.
Ken Seng
Ken Seng is a cinematographer known for his visually distinctive work on films such as "Obsessed."
-
E.
Soo Beng Kiang
Soo Beng Kiang is a former Malaysian badminton player best known as a top men's doubles specialist in the 1990s.
- 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47299cd7881908aac13b84acb61f7 |
completed | April 19, 2026, 6:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a023025ae808190ba3e9a875bfc68e9 |
completed | May 11, 2026, 7:38 p.m. |
| NEDg | Description generation | batch_6a023249cfb0819091d3fa8c3089f241 |
completed | May 11, 2026, 7:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0232ce696c81908d0e57ea214f88ce |
completed | May 11, 2026, 7:49 p.m. |
Created at: April 10, 2026, 10:05 a.m.