Triple
T3876217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Sumatra |
E92507
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Medan |
E22954
|
NE FINISHED |
How this triple was built (2 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: Medan | Statement: [North Sumatra, capital, Medan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Medan Context triple: [North Sumatra, capital, Medan]
-
A.
Medan
chosen
Medan is a major economic and cultural hub in northern Sumatra, known as one of Indonesia’s largest cities and a gateway to the region.
-
B.
Medan
Medan is a minor biblical figure mentioned in the Book of Genesis as one of the sons of Abraham by his wife Keturah.
-
C.
Padang
Padang is a major coastal city in western Indonesia known as the capital of West Sumatra and a cultural and culinary center of the Minangkabau people.
-
D.
Pekanbaru
Pekanbaru is a major commercial and transportation hub in central Sumatra, Indonesia, known for its oil industry and rapid urban growth.
-
E.
Batam
Batam is a major Indonesian industrial and transport hub located near Singapore, known for its free-trade zone status and rapidly growing economy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec706434819095e0d2b376adb548 |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5124f095881909143b624128ff569 |
completed | March 14, 2026, 7:46 a.m. |
Created at: March 9, 2026, 3:20 p.m.