Triple
T29972493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leuwidamar District |
E761355
|
entity |
| Predicate | hasRegencyCapitalNearby |
P192034
|
FINISHED |
| Object | Rangkasbitung (capital of Lebak Regency) |
—
|
NE NERFINISHED |
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: Rangkasbitung (capital of Lebak Regency) | Statement: [Leuwidamar District, hasRegencyCapitalNearby, Rangkasbitung (capital of Lebak Regency)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRegencyCapitalNearby Context triple: [Leuwidamar District, hasRegencyCapitalNearby, Rangkasbitung (capital of Lebak Regency)]
-
A.
hasRegencySeatNearby
chosen
Indicates that an entity has a regency seat located in close geographical proximity to it.
-
B.
regionCapitalNearby
Indicates that a capital city of a region is located close to the referenced place or entity.
-
C.
hasColonialCapitalNearby
Indicates that an entity is located close to a city that served as a colonial capital.
-
D.
hasDepartmentCapitalNearby
Indicates that the subject entity has a departmental capital city located in close geographical proximity to it.
-
E.
countyCapitalNearby
Indicates that a county’s capital city is located close to a specified place or entity.
- F. None of above.
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_69f22467626081908d5afea489590e96 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a003ffb548081908ba64fd2b570f42f |
completed | May 10, 2026, 8:21 a.m. |
| PD | Predicate disambiguation | batch_6a003fcecf788190a13dff38b9de3414 |
completed | May 10, 2026, 8:20 a.m. |
Created at: April 29, 2026, 6:32 p.m.