Triple
T16553081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banda Aceh |
E402120
|
entity |
| Predicate | connectedByFerryTo |
P1831
|
FINISHED |
| Object |
Sabang
Sabang is a small Indonesian city and popular tourist destination located on Weh Island off the northern tip of Sumatra.
|
E1220510
|
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: Sabang | Statement: [Banda Aceh, connectedByFerryTo, Sabang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sabang Context triple: [Banda Aceh, connectedByFerryTo, Sabang]
-
A.
Sabang
Sabang is a coastal barangay in Baler, Aurora, Philippines, known for its surfing beaches and tourism.
-
B.
Sabang
Sabang is a barangay (village-level administrative division) located in the municipality of Morong in the province of Bataan, Philippines.
-
C.
Banda Aceh
Banda Aceh is the largest city in Indonesia’s Aceh province, known as a historic center of Islamic culture and for being one of the areas hardest hit by the 2004 Indian Ocean tsunami.
-
D.
Labuan
Labuan is a coastal town in Banten, western Java, Indonesia, known as a gateway to nearby natural attractions and marine tourism areas.
-
E.
Labuan
Labuan is a federal territory of Malaysia comprising a main island and several smaller ones, known as an offshore financial center and duty-free port off the coast of Borneo.
- 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: Sabang Triple: [Banda Aceh, connectedByFerryTo, Sabang]
Generated description
Sabang is a small Indonesian city and popular tourist destination located on Weh Island off the northern tip of Sumatra.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sabang Target entity description: Sabang is a small Indonesian city and popular tourist destination located on Weh Island off the northern tip of Sumatra.
-
A.
Sabang
Sabang is a coastal barangay in Baler, Aurora, Philippines, known for its surfing beaches and tourism.
-
B.
Sabang
Sabang is a barangay (village-level administrative division) located in the municipality of Morong in the province of Bataan, Philippines.
-
C.
Banda Aceh
Banda Aceh is the largest city in Indonesia’s Aceh province, known as a historic center of Islamic culture and for being one of the areas hardest hit by the 2004 Indian Ocean tsunami.
-
D.
Labuan
Labuan is a coastal town in Banten, western Java, Indonesia, known as a gateway to nearby natural attractions and marine tourism areas.
-
E.
Labuan
Labuan is a federal territory of Malaysia comprising a main island and several smaller ones, known as an offshore financial center and duty-free port off the coast of Borneo.
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34fc6735481908b59bbf80fb3469b |
completed | April 18, 2026, 9:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0067b87b608190950b8f14e6aceed3 |
completed | May 10, 2026, 11:10 a.m. |
| NEDg | Description generation | batch_6a00686f87408190b7d8a41cd54735d8 |
completed | May 10, 2026, 11:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a006b6edd7081908730363b267253dd |
completed | May 10, 2026, 11:26 a.m. |
Created at: April 10, 2026, 5:15 a.m.