Triple
T6505112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southeast Sulawesi |
E149987
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Rumbia
Rumbia is a town in Indonesia’s Southeast Sulawesi province that serves as a local administrative and population center.
|
E600658
|
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: Rumbia | Statement: [Southeast Sulawesi, hasCity, Rumbia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rumbia Context triple: [Southeast Sulawesi, hasCity, Rumbia]
-
A.
Rumueme
Rumueme is a prominent urban community in Rivers State, Nigeria, forming part of the greater Port Harcourt metropolitan area.
-
B.
Fiambalá
Fiambalá is a small town in northwestern Argentina known for its high-altitude vineyards, desert landscapes, and nearby Andean mountain passes.
-
C.
Rauco
Rauco is a rural municipality and commune in central Chile’s Maule Region, known for its agricultural activities and proximity to the city of Curicó.
-
D.
Marulanda
Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
-
E.
Urambo
Urambo is a town and district headquarters in western Tanzania known historically for tobacco production and its location within the Tabora Region.
- 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: Rumbia Triple: [Southeast Sulawesi, hasCity, Rumbia]
Generated description
Rumbia is a town in Indonesia’s Southeast Sulawesi province that serves as a local administrative and population center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rumbia Target entity description: Rumbia is a town in Indonesia’s Southeast Sulawesi province that serves as a local administrative and population center.
-
A.
Rumueme
Rumueme is a prominent urban community in Rivers State, Nigeria, forming part of the greater Port Harcourt metropolitan area.
-
B.
Fiambalá
Fiambalá is a small town in northwestern Argentina known for its high-altitude vineyards, desert landscapes, and nearby Andean mountain passes.
-
C.
Rauco
Rauco is a rural municipality and commune in central Chile’s Maule Region, known for its agricultural activities and proximity to the city of Curicó.
-
D.
Marulanda
Marulanda is a small municipality and town located in the Caldas Department of Colombia, known for its rural Andean landscapes and agricultural economy.
-
E.
Urambo
Urambo is a town and district headquarters in western Tanzania known historically for tobacco production and its location within the Tabora Region.
- 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69966ff708190902c88cb6b48e5d7 |
completed | March 27, 2026, 2:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb43db608190b785e77f6850bb6f |
completed | March 27, 2026, 6:24 p.m. |
| NEDg | Description generation | batch_69c6cc96edd08190b0c0f1b49dd64160 |
completed | March 27, 2026, 6:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6cd8d15ec8190be5a8c5e3f201139 |
completed | March 27, 2026, 6:33 p.m. |
Created at: March 27, 2026, 1:43 p.m.