Triple
T37639955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Killer Highway |
E936588
|
entity |
| Predicate | hasCauseForReputation |
P20748
|
FINISHED |
| Object | historically high accident statistics |
—
|
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: historically high accident statistics | Statement: [Killer Highway, hasCauseForReputation, historically high accident statistics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCauseForReputation Context triple: [Killer Highway, hasCauseForReputation, historically high accident statistics]
-
A.
causeOfReputation
chosen
Indicates that one entity is the reason or source for another entity’s reputation.
-
B.
hasMannerOfNotoriety
Indicates that an entity is known or recognized in a particular way, specifying the manner or type of its notoriety.
-
C.
hasNotableReputation
Indicates that an entity is widely recognized or distinguished for a particular quality, achievement, or characteristic.
-
D.
hasQuirkyReputation
Indicates that an entity is regarded by others as having an unusual, eccentric, or unconventional character or style.
-
E.
reputationBeforeScandal
Indicates the reputation or public standing an entity had prior to a specific scandal or damaging event.
- 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_69f76ed31d8881908405da6c6d2f0463 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a0027e4a59481909417b2531daaf480 |
completed | May 10, 2026, 6:38 a.m. |
| PD | Predicate disambiguation | batch_6a0026a42bc08190ad3322ce625a523a |
completed | May 10, 2026, 6:33 a.m. |
Created at: May 3, 2026, 4:18 p.m.