Triple
T14231693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sumpah Palapa |
E352765
|
entity |
| Predicate | mentionedPlaces |
P10233
|
FINISHED |
| Object | Palembang |
E88175
|
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: Palembang | Statement: [Sumpah Palapa, mentionedPlaces, Palembang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Palembang Context triple: [Sumpah Palapa, mentionedPlaces, Palembang]
-
A.
Palembang
chosen
Palembang is a major Indonesian city on the island of Sumatra, historically known as the center of the Srivijaya maritime empire and now an important economic and cultural hub.
-
B.
Banjarmasin
Banjarmasin is a major riverine city in South Kalimantan, Indonesia, known for its historic floating markets and strategic location on the island of Borneo.
-
C.
Makassar
Makassar is a major port city on the southwest coast of Sulawesi known historically as a key maritime trading hub in eastern Indonesia.
-
D.
Banjarbaru
Banjarbaru is a rapidly developing city in Indonesia that serves as the capital and administrative center of South Kalimantan province on the island of Borneo.
-
E.
Martapura
Martapura is a prominent town in Indonesia’s South Kalimantan province, known as a center for Islamic education and its traditional diamond and gemstone markets.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de622cdd6481908befa179a9675bb5 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd46794e9c819093312a2c304e554e |
completed | May 8, 2026, 2:12 a.m. |
Created at: April 10, 2026, 1:07 a.m.