Triple
T970351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Money Heist |
E20929
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Nairobi
Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
|
E115622
|
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: Nairobi | Statement: [Money Heist, character, Nairobi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nairobi Context triple: [Money Heist, character, Nairobi]
-
A.
Nairobi
Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
-
B.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
C.
Dodoma
Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
-
D.
Kampala
Kampala is the capital and largest city of Uganda, serving as the country’s political, economic, and cultural center.
-
E.
Dar es Salaam
Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
- 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: Nairobi Triple: [Money Heist, character, Nairobi]
Generated description
Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nairobi Target entity description: Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
-
A.
Nairobi
Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
-
B.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
C.
Dodoma
Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
-
D.
Kampala
Kampala is the capital and largest city of Uganda, serving as the country’s political, economic, and cultural center.
-
E.
Dar es Salaam
Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b4497d688190b59c3a195e377080 |
completed | March 1, 2026, 9:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac1cd9705c8190adf1fb72188cc84e |
completed | March 7, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_69ac1db013e481908aaa08f4be7e4182 |
completed | March 7, 2026, 12:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac1e187a588190912d88d1a3438349 |
completed | March 7, 2026, 12:46 p.m. |
Created at: March 1, 2026, 7:40 p.m.