Triple
T7721819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Depok |
E175029
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
Kota Petir
Kota Petir is a popular nickname for the Indonesian city of Depok, highlighting its reputation for frequent and intense thunderstorms.
|
E684137
|
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: Kota Petir | Statement: [Depok, hasNickname, Kota Petir]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kota Petir Context triple: [Depok, hasNickname, Kota Petir]
-
A.
Kota Hujan
Kota Hujan is the popular nickname for Bogor, an Indonesian city renowned for its frequent rainfall and cool, wet climate.
-
B.
Kota Hujan
Kota Hujan is a popular nickname for Padang Panjang, a small highland city in West Sumatra, Indonesia, known for its frequent rainfall and cool climate.
-
C.
Himu
Himu is a popular fictional wanderer-philosopher character in Bangladeshi literature, known for his yellow panjabi, barefoot roaming, and unconventional outlook on life.
-
D.
Kabale
Kabale is a town in southwestern Uganda that serves as a key regional center and gateway to nearby attractions such as Bwindi Impenetrable National Park.
-
E.
Kamitsumaki
Kamitsumaki is the first volume of the ancient Japanese chronicle Kojiki, focusing on Shinto creation myths and the age of the gods.
- 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: Kota Petir Triple: [Depok, hasNickname, Kota Petir]
Generated description
Kota Petir is a popular nickname for the Indonesian city of Depok, highlighting its reputation for frequent and intense thunderstorms.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kota Petir Target entity description: Kota Petir is a popular nickname for the Indonesian city of Depok, highlighting its reputation for frequent and intense thunderstorms.
-
A.
Kota Hujan
Kota Hujan is the popular nickname for Bogor, an Indonesian city renowned for its frequent rainfall and cool, wet climate.
-
B.
Kota Hujan
Kota Hujan is a popular nickname for Padang Panjang, a small highland city in West Sumatra, Indonesia, known for its frequent rainfall and cool climate.
-
C.
Himu
Himu is a popular fictional wanderer-philosopher character in Bangladeshi literature, known for his yellow panjabi, barefoot roaming, and unconventional outlook on life.
-
D.
Kabale
Kabale is a town in southwestern Uganda that serves as a key regional center and gateway to nearby attractions such as Bwindi Impenetrable National Park.
-
E.
Kamitsumaki
Kamitsumaki is the first volume of the ancient Japanese chronicle Kojiki, focusing on Shinto creation myths and the age of the gods.
- 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_69c6995d541c81909eaa646b1a8369a9 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702f1786881908b025d8986e5f1fa |
completed | March 27, 2026, 10:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b51b612881909f20a6b777db348c |
completed | March 29, 2026, 5:14 a.m. |
| NEDg | Description generation | batch_69c8b639df28819095af623e96181d9c |
completed | March 29, 2026, 5:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8b68f44148190b08882625db98f96 |
completed | March 29, 2026, 5:20 a.m. |
Created at: March 27, 2026, 4:05 p.m.