Triple
T7227187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Java |
E154809
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Banjarnegara
Banjarnegara is a regency-level town in Central Java, Indonesia, known as an administrative and economic center in the region.
|
E672941
|
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: Banjarnegara | Statement: [Central Java, hasMajorCity, Banjarnegara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Banjarnegara Context triple: [Central Java, hasMajorCity, Banjarnegara]
-
A.
Kebumen
Kebumen is a regency-level town in southern Central Java, Indonesia, known for its agricultural surroundings, coastal areas, and proximity to karst landscapes and caves.
-
B.
Purworejo
Purworejo is a regency in Central Java, Indonesia, known for its agricultural landscape and proximity to the southern coast of Java.
-
C.
Temanggung
Temanggung is a regency in Central Java, Indonesia, known for its tobacco plantations and mountainous landscape near Mount Sindoro and Mount Sumbing.
-
D.
Cilacap
Cilacap is a coastal city in southern Central Java, Indonesia, known for its major port, industrial facilities, and proximity to the Indian Ocean.
-
E.
Trenggalek
Trenggalek is a regency and its capital town in southern East Java, Indonesia, known for its coastal landscapes, caves, and agricultural economy.
- 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: Banjarnegara Triple: [Central Java, hasMajorCity, Banjarnegara]
Generated description
Banjarnegara is a regency-level town in Central Java, Indonesia, known as an administrative and economic center in the region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Banjarnegara Target entity description: Banjarnegara is a regency-level town in Central Java, Indonesia, known as an administrative and economic center in the region.
-
A.
Kebumen
Kebumen is a regency-level town in southern Central Java, Indonesia, known for its agricultural surroundings, coastal areas, and proximity to karst landscapes and caves.
-
B.
Purworejo
Purworejo is a regency in Central Java, Indonesia, known for its agricultural landscape and proximity to the southern coast of Java.
-
C.
Temanggung
Temanggung is a regency in Central Java, Indonesia, known for its tobacco plantations and mountainous landscape near Mount Sindoro and Mount Sumbing.
-
D.
Cilacap
Cilacap is a coastal city in southern Central Java, Indonesia, known for its major port, industrial facilities, and proximity to the Indian Ocean.
-
E.
Trenggalek
Trenggalek is a regency and its capital town in southern East Java, Indonesia, known for its coastal landscapes, caves, and agricultural economy.
- 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_69c68811dd1c8190ac460bb39e64e1f0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6e9df72cc81908d1c04e6e310fbb4 |
completed | March 27, 2026, 8:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8569fbf4081909897b0e5456cd66a |
completed | March 28, 2026, 10:30 p.m. |
| NEDg | Description generation | batch_69c857ca74808190968fe68a0e4cc77b |
completed | March 28, 2026, 10:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c858184a0c81909ce2789371e37c05 |
completed | March 28, 2026, 10:37 p.m. |
Created at: March 27, 2026, 2:54 p.m.