Triple
T35873909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilauea Iki 1959 eruption |
E1037305
|
entity |
| Predicate | numberOfFountainingEpisodes |
P206084
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [Kilauea Iki 1959 eruption, numberOfFountainingEpisodes, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFountainingEpisodes Context triple: [Kilauea Iki 1959 eruption, numberOfFountainingEpisodes, 17]
-
A.
numberOfFountains
Indicates the quantitative relationship specifying how many fountains are associated with a given entity.
-
B.
floodEventsCount
Indicates the number of flood events that have occurred or been recorded for a given context or entity.
-
C.
numberOfFissures
Indicates the count of distinct fissures associated with a given entity or structure.
-
D.
eruptionFrequency
Indicates how often an eruption event occurs within a given time period.
-
E.
hasEruptionEpisodes
Indicates that an entity (such as a volcano or geyser) undergoes one or more distinct eruption events over time.
- 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_69f76e1e701c8190a4990d4978ce4fe6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037ce53de881908cf14141cf3bc570 |
completed | May 12, 2026, 7:17 p.m. |
Created at: May 3, 2026, 4:06 p.m.