Triple
T17599701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skyline Rotorua |
E428660
|
entity |
| Predicate | lugeTrackVariety |
P128178
|
FINISHED |
| Object | multiple tracks of varying difficulty |
—
|
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: multiple tracks of varying difficulty | Statement: [Skyline Rotorua, lugeTrackVariety, multiple tracks of varying difficulty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lugeTrackVariety Context triple: [Skyline Rotorua, lugeTrackVariety, multiple tracks of varying difficulty]
-
A.
lugeVenue
Indicates that a location serves or is used as a venue for luge events or activities.
-
B.
competitionLengthLugeWomen
Indicates the duration or length of a women's luge competition.
-
C.
bobsleighVenue
Indicates that a location serves as the venue where a bobsleigh event or activity takes place.
-
D.
competitionLengthLugeMen
Indicates the duration or length of a men’s luge competition event.
-
E.
skatingElementType
Indicates the specific type or category of a skating element performed or referenced in a skating activity or program.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c4812d48190bf8e899fa8f7fbe4 |
completed | April 19, 2026, 5:46 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fff0348190b899a32da537eaca |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb50b448190a59dd4be33c76db7 |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:51 a.m.