Triple
T8398020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Perak River |
E198101
|
entity |
| Predicate | rankingByLengthInPeninsularMalaysia |
P82012
|
FINISHED |
| Object | second longest river in Peninsular Malaysia |
—
|
LITERAL 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: second longest river in Peninsular Malaysia | Statement: [Perak River, rankingByLengthInPeninsularMalaysia, second longest river in Peninsular Malaysia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByLengthInPeninsularMalaysia Context triple: [Perak River, rankingByLengthInPeninsularMalaysia, second longest river in Peninsular Malaysia]
-
A.
areaRankInMalaysia
Indicates the relative position of an entity in a size-based ranking by area within Malaysia.
-
B.
rankByLengthInAsia
Indicates that entities are ordered or compared based on their length within the context of Asia.
-
C.
nearCityOnMalaysianSide
Indicates that one entity is located close to a particular city that lies on the Malaysian side of a border or region.
-
D.
statusInMalaysia
Indicates the legal, social, or official standing or condition an entity holds specifically within the context of Malaysia.
-
E.
distanceFromKualaLumpur
Indicates the spatial distance between a given location or entity and Kuala Lumpur.
- F. None of above. chosen
Provenance (4 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_69ca82f816bc8190ab321c07d72208c1 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb818a8dc081908efd5d7f910322e7 |
completed | March 31, 2026, 8:10 a.m. |
| PD | Predicate disambiguation | batch_69cb70d24b248190a326aa6804f942b5 |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb77690720819099de1e22b84a9563 |
completed | March 31, 2026, 7:27 a.m. |
Created at: March 30, 2026, 6:04 p.m.