Triple
T14735010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuen Mun |
E346180
|
entity |
| Predicate | hasPopulationRankInHongKong |
P25930
|
FINISHED |
| Object | one of the most populous districts |
—
|
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: one of the most populous districts | Statement: [Tuen Mun, hasPopulationRankInHongKong, one of the most populous districts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInHongKong Context triple: [Tuen Mun, hasPopulationRankInHongKong, one of the most populous districts]
-
A.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
B.
hasPopulationRankInRegion
chosen
Indicates that an entity has a specific population-based rank or position within a defined geographic region.
-
C.
precededByTallestInHongKong
Indicates that one entity comes immediately before the tallest entity in Hong Kong in a specified sequence or ordering.
-
D.
succeededByTallestInHongKong
Indicates that one entity is succeeded or replaced by another entity that is the tallest in Hong Kong.
-
E.
hasAreaRankInTaiwan
Indicates the relative ranking of an entity by its area size compared to other entities within Taiwan.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec73114cc819088e1101b689fc70b |
completed | April 14, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69de8bf9331481909582045cd567d91f |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:29 a.m.