Triple
T1375641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bogor |
E29215
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
Rain City
Rain City is the popular nickname of Bogor, an Indonesian city renowned for its frequent rainfall and cool, lush climate.
|
E159022
|
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: Rain City | Statement: [Bogor, hasNickname, Rain City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rain City Context triple: [Bogor, hasNickname, Rain City]
-
A.
Rain City
Rain City is a popular nickname for Vancouver, a coastal Canadian city known for its frequent rainfall and lush, temperate climate.
-
B.
Cream City
Cream City is a nickname for Milwaukee, Wisconsin, derived from the distinctive light-colored cream brick used in many of its historic buildings.
-
C.
Queen City
Queen City is the popular nickname for Charlotte, North Carolina, a major financial and cultural hub in the southeastern United States.
-
D.
Queen City
Queen City is a popular nickname for Cincinnati, Ohio, highlighting its historic prominence and cultural importance in the region.
-
E.
Mill City
Mill City is the nickname for Lowell, Massachusetts, a historic New England city known for its 19th-century textile mills and role in the American Industrial Revolution.
- 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: Rain City Triple: [Bogor, hasNickname, Rain City]
Generated description
Rain City is the popular nickname of Bogor, an Indonesian city renowned for its frequent rainfall and cool, lush climate.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rain City Target entity description: Rain City is the popular nickname of Bogor, an Indonesian city renowned for its frequent rainfall and cool, lush climate.
-
A.
Rain City
Rain City is a popular nickname for Vancouver, a coastal Canadian city known for its frequent rainfall and lush, temperate climate.
-
B.
Cream City
Cream City is a nickname for Milwaukee, Wisconsin, derived from the distinctive light-colored cream brick used in many of its historic buildings.
-
C.
Queen City
Queen City is the popular nickname for Charlotte, North Carolina, a major financial and cultural hub in the southeastern United States.
-
D.
Queen City
Queen City is a popular nickname for Cincinnati, Ohio, highlighting its historic prominence and cultural importance in the region.
-
E.
Mill City
Mill City is the nickname for Lowell, Massachusetts, a historic New England city known for its 19th-century textile mills and role in the American Industrial Revolution.
- 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2f9b51c8190ad52fd8c151499be |
completed | March 1, 2026, 10:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acd485e7e48190b4c2dcec53ebe195 |
completed | March 8, 2026, 1:44 a.m. |
| NEDg | Description generation | batch_69acd971f3088190adc54e5eed0e3e0b |
completed | March 8, 2026, 2:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69acda1c9fac8190a825533837f612b2 |
completed | March 8, 2026, 2:08 a.m. |
Created at: March 1, 2026, 7:59 p.m.