Triple
T15535129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baby Park |
E370320
|
entity |
| Predicate | lapCountVariant |
P119065
|
FINISHED |
| Object | 7 laps in Mario Kart: Double Dash!! |
—
|
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: 7 laps in Mario Kart: Double Dash!! | Statement: [Baby Park, lapCountVariant, 7 laps in Mario Kart: Double Dash!!]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lapCountVariant Context triple: [Baby Park, lapCountVariant, 7 laps in Mario Kart: Double Dash!!]
-
A.
variantCount
Indicates the number of distinct variants associated with a given entity or item.
-
B.
movementCount
Indicates the number of times a movement or relocation action has occurred between the related entities.
-
C.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
D.
branchCount
Indicates the number of branches associated with a given entity or structure.
-
E.
alternativeCounting
Indicates that there exists another valid way of counting or enumerating the same set of items or events, distinct from the primary counting method.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0442e327c8190b4b879c8a3cd38e3 |
completed | April 16, 2026, 2:06 a.m. |
| PD | Predicate disambiguation | batch_69deda7a95c48190bbe29fadcf17191a |
completed | April 15, 2026, 12:23 a.m. |
| PDg | Predicate description generation | batch_69dff7f05f708190850f1d8782e132b0 |
completed | April 15, 2026, 8:41 p.m. |
Created at: April 10, 2026, 4:06 a.m.