Triple
T1330188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIRST Robotics Competition |
E28623
|
entity |
| Predicate | robotWeightLimit |
P26160
|
FINISHED |
| Object | approximately 125 pounds without battery and bumpers |
—
|
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: approximately 125 pounds without battery and bumpers | Statement: [FIRST Robotics Competition, robotWeightLimit, approximately 125 pounds without battery and bumpers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: robotWeightLimit Context triple: [FIRST Robotics Competition, robotWeightLimit, approximately 125 pounds without battery and bumpers]
-
A.
weightLimitInKilograms
Indicates the maximum allowable weight for something, expressed in kilograms.
-
B.
weightLimitInPounds
chosen
Indicates the maximum allowable weight for something, expressed in pounds.
-
C.
emptyWeight
Indicates the weight of an object or vehicle when it is empty, excluding any load, cargo, or passengers.
-
D.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
E.
associatedVehicleWeightClass
Indicates the weight classification category that is linked or assigned to a particular vehicle.
- 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1c449bc8190acb6603a0ddac7fd |
completed | March 1, 2026, 10:46 p.m. |
| PD | Predicate disambiguation | batch_69a4beef6a188190996f8775bdda8f6c |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.