Triple
T405834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tesla Model X |
E9380
|
entity |
| Predicate | emissions |
P5628
|
FINISHED |
| Object | zero tailpipe emissions |
—
|
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: zero tailpipe emissions | Statement: [Tesla Model X, emissions, zero tailpipe emissions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emissions Context triple: [Tesla Model X, emissions, zero tailpipe emissions]
-
A.
hasEnvironmentalImpactOn
chosen
Indicates that one entity affects or alters the environmental conditions, quality, or ecological state of another entity.
-
B.
environmentalIssue
Indicates that something is a problem or concern related to the natural environment, such as harm, risk, or negative impact on ecosystems or resources.
-
C.
drives
Indicates that one entity operates and controls the movement of a vehicle or similar conveyance transporting themselves or others.
-
D.
sustainabilityFocus
Indicates a relationship where an entity prioritizes or emphasizes environmental, social, or long-term resource sustainability in its actions, policies, or strategies.
-
E.
losses
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ecbc00508190bbb602179273f29c |
completed | Feb. 28, 2026, 1:25 p.m. |
| PD | Predicate disambiguation | batch_69a2e971a3a481909e6b075f25dd234a |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.