Triple
T22657404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bintan |
E559263
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Tanjung Uban
Tanjung Uban is a coastal town on Bintan Island in Indonesia’s Riau Islands province, known as a local transport and commercial hub.
|
E1547988
|
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: Tanjung Uban | Statement: [Bintan, hasTown, Tanjung Uban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanjung Uban Context triple: [Bintan, hasTown, Tanjung Uban]
-
A.
Tanjung Aan
Tanjung Aan is a scenic white-sand beach and popular coastal tourist destination on the southern coast of Lombok, Indonesia.
-
B.
Tanjung Raya
Tanjung Raya is a district in West Sumatra, Indonesia, known for encompassing the scenic area around Lake Maninjau and its surrounding highland landscapes.
-
C.
Tanjung Rambutan
Tanjung Rambutan is a small town in Perak, Malaysia, historically known for its large psychiatric hospital and its location within the Kinta Valley.
-
D.
Tanjung Bira
Tanjung Bira is a popular beach destination in South Sulawesi, Indonesia, known for its white sand, clear turquoise waters, and diving and snorkeling spots.
-
E.
Tanjung Usi
Tanjung Usi is a notable scuba diving site in Indonesia’s Bangka Archipelago, known for its rich marine life and underwater scenery.
- 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: Tanjung Uban Triple: [Bintan, hasTown, Tanjung Uban]
Generated description
Tanjung Uban is a coastal town on Bintan Island in Indonesia’s Riau Islands province, known as a local transport and commercial hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tanjung Uban Target entity description: Tanjung Uban is a coastal town on Bintan Island in Indonesia’s Riau Islands province, known as a local transport and commercial hub.
-
A.
Tanjung Aan
Tanjung Aan is a scenic white-sand beach and popular coastal tourist destination on the southern coast of Lombok, Indonesia.
-
B.
Tanjung Raya
Tanjung Raya is a district in West Sumatra, Indonesia, known for encompassing the scenic area around Lake Maninjau and its surrounding highland landscapes.
-
C.
Tanjung Rambutan
Tanjung Rambutan is a small town in Perak, Malaysia, historically known for its large psychiatric hospital and its location within the Kinta Valley.
-
D.
Tanjung Bira
Tanjung Bira is a popular beach destination in South Sulawesi, Indonesia, known for its white sand, clear turquoise waters, and diving and snorkeling spots.
-
E.
Tanjung Usi
Tanjung Usi is a notable scuba diving site in Indonesia’s Bangka Archipelago, known for its rich marine life and underwater scenery.
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765c62bc8190b3fcde76d6b6dfb6 |
completed | April 29, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b586cd2b8819095ff984b9c14c1ec |
completed | May 18, 2026, 6:20 p.m. |
| NEDg | Description generation | batch_6a0b6f5f8bd08190b325b9dd91e199e1 |
completed | May 18, 2026, 7:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b7044c94c81909a43b04f40af2a15 |
completed | May 18, 2026, 8:02 p.m. |
Created at: April 17, 2026, 3:06 p.m.