Triple
T24936564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Have Blue |
E623326
|
entity |
| Predicate | crashHistory |
P157259
|
FINISHED |
| Object | both prototypes lost in accidents |
—
|
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: both prototypes lost in accidents | Statement: [Have Blue, crashHistory, both prototypes lost in accidents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crashHistory Context triple: [Have Blue, crashHistory, both prototypes lost in accidents]
-
A.
crash
Indicates that one entity collides violently with another entity or surface, often resulting in damage or disruption.
-
B.
crashResultedIn
Indicates that a crash event caused or led to a particular outcome, consequence, or result.
-
C.
crashCountry
Indicates that an aviation crash event occurred within the territory or jurisdiction of a specified country.
-
D.
estimatedHistory
Indicates that there is an inferred or approximated record of past states, events, or values associated with an entity or relationship.
-
E.
associatedCrashSite
Indicates that an entity is linked or related to a particular crash site, typically as the site where an incident involving that entity occurred or is recorded.
- 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f423d5a0f48190b4df532471cd37d1 |
completed | May 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69f4210130d08190ae30b7943f7a0bbc |
completed | May 1, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f423637bec8190a1701421ac86a3b7 |
completed | May 1, 2026, 3:52 a.m. |
Created at: April 18, 2026, 5:30 a.m.