Triple
T1361849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kofi Annan |
E29113
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Annan
Annan is the surname of Kofi Annan, the Ghanaian diplomat and former Secretary-General of the United Nations.
|
E156960
|
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: Annan | Statement: [Kofi Annan, familyName, Annan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annan Context triple: [Kofi Annan, familyName, Annan]
-
A.
Mariinka
Mariinka is a renowned historic opera and ballet theatre in Saint Petersburg, Russia, celebrated for its world-class performances and prestigious resident companies.
-
B.
Akure
Akure is the capital city of Ondo State in southwestern Nigeria, known as an important administrative and commercial center in the region.
-
C.
Senja
Senja is Norway’s second-largest island, renowned for its dramatic coastal mountains, fishing villages, and scenic Arctic landscapes.
-
D.
Notodden
Notodden is a town and municipality in Vestfold og Telemark county, Norway, known for its industrial heritage and annual blues festival.
-
E.
Muko
Muko is a small city in Japan’s Kyoto Prefecture, known for its residential character and proximity to Kyoto City.
- 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: Annan Triple: [Kofi Annan, familyName, Annan]
Generated description
Annan is the surname of Kofi Annan, the Ghanaian diplomat and former Secretary-General of the United Nations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Annan Target entity description: Annan is the surname of Kofi Annan, the Ghanaian diplomat and former Secretary-General of the United Nations.
-
A.
Mariinka
Mariinka is a renowned historic opera and ballet theatre in Saint Petersburg, Russia, celebrated for its world-class performances and prestigious resident companies.
-
B.
Akure
Akure is the capital city of Ondo State in southwestern Nigeria, known as an important administrative and commercial center in the region.
-
C.
Senja
Senja is Norway’s second-largest island, renowned for its dramatic coastal mountains, fishing villages, and scenic Arctic landscapes.
-
D.
Notodden
Notodden is a town and municipality in Vestfold og Telemark county, Norway, known for its industrial heritage and annual blues festival.
-
E.
Muko
Muko is a small city in Japan’s Kyoto Prefecture, known for its residential character and proximity to Kyoto City.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b2fb448190bef31375169b4666 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acce7449548190917277dbc715cde4 |
completed | March 8, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69acd06d000481909f6d934e857236f0 |
completed | March 8, 2026, 1:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acd17b8c508190812b241d7906992b |
completed | March 8, 2026, 1:31 a.m. |
Created at: March 1, 2026, 7:57 p.m.