Triple
T24580777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PNG Kumuls |
E608241
|
entity |
| Predicate | nationalPopularity |
P156422
|
FINISHED |
| Object | very high in Papua New Guinea |
—
|
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: very high in Papua New Guinea | Statement: [PNG Kumuls, nationalPopularity, very high in Papua New Guinea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nationalPopularity Context triple: [PNG Kumuls, nationalPopularity, very high in Papua New Guinea]
-
A.
internationalPopularity
Indicates that something is widely recognized, appreciated, or favored across multiple countries or cultures.
-
B.
nationalTopRank
Indicates that an entity holds the highest rank or leading position at the national level within a specified domain or category.
-
C.
namePopularityType
Indicates the category or type of popularity associated with a given name (e.g., how or in what way the name is considered popular).
-
D.
namePopularityRegion
Indicates the geographic region or area in which a particular name has a certain level of popularity or usage.
-
E.
frequencyRankInUnitedStates
Indicates the relative position of something in an ordered list based on how frequently it occurs within the United States.
- 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_69e2c4ce89248190ad99e18f0638dfbb |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2a982db00819081038adce05c0b52 |
completed | April 30, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f2a6c1f07081908edf0b521767e79b |
completed | April 30, 2026, 12:48 a.m. |
| PDg | Predicate description generation | batch_69f2a846c5bc81909ba50cee483bea91 |
completed | April 30, 2026, 12:54 a.m. |
Created at: April 18, 2026, 2:29 a.m.