Triple
T136489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Detroit Big Three automakers |
E2757
|
entity |
| Predicate | historicallyInfluenced |
P9
|
FINISHED |
| Object | U.S. transportation policy |
—
|
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: U.S. transportation policy | Statement: [Detroit Big Three automakers, historicallyInfluenced, U.S. transportation policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicallyInfluenced Context triple: [Detroit Big Three automakers, historicallyInfluenced, U.S. transportation policy]
-
A.
historicalImpact
Indicates the influence or lasting effects that an entity, event, or action has had on subsequent history or historical developments.
-
B.
hasHistoricalSignificance
Indicates that something possesses notable importance or influence within a historical context or period.
-
C.
historicallyBorneBy
Indicates that an entity has carried, possessed, or used another entity (such as a name, title, or symbol) at some point in the past.
-
D.
hasHistoricalContext
Indicates that something is related to, influenced by, or best understood in light of specific past events, conditions, or time periods.
-
E.
influenced
chosen
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
- 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_69a2520c0f3481908b0ed054a2fca8d0 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a257a4edf081908c494c8370c76b9a |
completed | Feb. 28, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69a25652efdc8190b85b33735a9e6370 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:30 a.m.