Triple
T1351705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ulsan |
E28895
|
entity |
| Predicate | hasPopulationRankInSouthKorea |
P25930
|
FINISHED |
| Object | one of the largest cities |
—
|
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 largest cities | Statement: [Ulsan, hasPopulationRankInSouthKorea, one of the largest cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationRankInSouthKorea Context triple: [Ulsan, hasPopulationRankInSouthKorea, one of the largest cities]
-
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.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
D.
populationRankInVietnam
Indicates the relative position of an entity in terms of population size compared to other entities within Vietnam.
-
E.
populationRankInIndonesia
Indicates the relative position of an entity in terms of population size compared to other entities within Indonesia.
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c26b1b4881908ae4b1b2c9b268a0 |
completed | March 1, 2026, 10:49 p.m. |
| PD | Predicate disambiguation | batch_69a4bef5857c81909ae984feb85a26ca |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:56 p.m.