Triple
T8601718
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1000 m speed skating at the 1998 Winter Olympics |
E203691
|
entity |
| Predicate | lapCount |
P41575
|
FINISHED |
| Object | 2.5 laps |
—
|
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: 2.5 laps | Statement: [1000 m speed skating at the 1998 Winter Olympics, lapCount, 2.5 laps]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lapCount Context triple: [1000 m speed skating at the 1998 Winter Olympics, lapCount, 2.5 laps]
-
A.
movementCount
Indicates the number of times a movement or relocation action has occurred between the related entities.
-
B.
count
chosen
Indicates the numerical quantity or total number of instances of a specified entity or event.
-
C.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
-
D.
numberOfCounts
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
E.
stepCount
Indicates the number of discrete steps taken or required in a process, sequence, or movement.
- 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_69ca832b56948190ba751cec255308f1 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46da609881909a6d851915e8df14 |
completed | March 31, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cc454eb2908190acf0e4336bc67e7b |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:24 p.m.