Triple
T11086989
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corkscrew (Michigan’s Adventure) |
E262145
|
entity |
| Predicate | numberOfInversions |
P78083
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Corkscrew (Michigan’s Adventure), numberOfInversions, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfInversions Context triple: [Corkscrew (Michigan’s Adventure), numberOfInversions, 2]
-
A.
hasInversionCount
chosen
Indicates that there is a specific number of pairwise order inversions present in a given sequence or arrangement.
-
B.
inversions
Indicates a relationship where the usual order, position, or hierarchy between elements is reversed or turned upside down.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
numberOfQueries
Indicates the total count of queries associated with or performed in a given context or entity.
-
E.
numberOfPairsUsed
Indicates the quantity of distinct pairs involved or utilized in a given context or operation.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799c3ed9c8190a3f5cdf1fe0e74a2 |
completed | April 9, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69d744185a5881909ba4cf151d1798ec |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.