Triple
T1477902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banten |
E30884
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Cilegon
Cilegon is an industrial port city in western Java, Indonesia, known for its steel industry and strategic location near the Sunda Strait.
|
E186493
|
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: Cilegon | Statement: [Banten, hasMajorCity, Cilegon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cilegon Context triple: [Banten, hasMajorCity, Cilegon]
-
A.
Serang
Serang is the capital city of Banten Province on the western tip of Java, Indonesia, serving as an important regional administrative and economic center.
-
B.
Tangerang
Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
-
C.
Sukabumi
Sukabumi is a city in southwestern West Java, Indonesia, known for its cool climate, surrounding highlands, and proximity to popular natural attractions.
-
D.
Bogor
Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
-
E.
Bandar Lampung
Bandar Lampung is a major port city in southern Sumatra, Indonesia, serving as the capital of Lampung Province and a key gateway between the island and Java.
- 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: Cilegon Triple: [Banten, hasMajorCity, Cilegon]
Generated description
Cilegon is an industrial port city in western Java, Indonesia, known for its steel industry and strategic location near the Sunda Strait.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cilegon Target entity description: Cilegon is an industrial port city in western Java, Indonesia, known for its steel industry and strategic location near the Sunda Strait.
-
A.
Serang
Serang is the capital city of Banten Province on the western tip of Java, Indonesia, serving as an important regional administrative and economic center.
-
B.
Tangerang
Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
-
C.
Sukabumi
Sukabumi is a city in southwestern West Java, Indonesia, known for its cool climate, surrounding highlands, and proximity to popular natural attractions.
-
D.
Bogor
Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
-
E.
Bandar Lampung
Bandar Lampung is a major port city in southern Sumatra, Indonesia, serving as the capital of Lampung Province and a key gateway between the island and Java.
- 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c605d4c0819088ab06678b2ba6f3 |
completed | March 1, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad6085199c8190868aef3c28842d4e |
completed | March 8, 2026, 11:41 a.m. |
| NEDg | Description generation | batch_69ad620fe35481909bf4751001e29161 |
completed | March 8, 2026, 11:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad626d42388190b6a961a84333bd21 |
completed | March 8, 2026, 11:50 a.m. |
Created at: March 1, 2026, 8:11 p.m.