Triple
T9543919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vestas V80 |
E230232
|
entity |
| Predicate | powerRating |
P89713
|
FINISHED |
| Object | 2 MW |
—
|
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 MW | Statement: [Vestas V80, powerRating, 2 MW]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: powerRating Context triple: [Vestas V80, powerRating, 2 MW]
-
A.
peakRanking
Indicates the highest position or rank an entity has ever achieved within a specified ranking system or context.
-
B.
rankingPoints
Indicates the number of points assigned to an entity based on its position or performance in a ranking or competition.
-
C.
peakRating
Indicates the highest rating value that has been achieved or recorded for an entity over a given period or context.
-
D.
SSRank
Indicates a ranking relationship that orders entities based on their social status, standing, or relative importance within a specified context.
-
E.
careerPasserRating
Indicates a player's overall passer rating calculated across their entire career, summarizing their long-term passing performance.
- 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_69ca847c70b8819088a0a0bad64a50d6 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd98ebd4148190b71b134d7545fe35 |
completed | April 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69ccd58bd21881908b860e3ee469af13 |
completed | April 1, 2026, 8:21 a.m. |
| PDg | Predicate description generation | batch_69ccd93e90048190a2b0d7c5c195ba98 |
completed | April 1, 2026, 8:37 a.m. |
Created at: March 30, 2026, 8:01 p.m.