Triple
T33308171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TK Strand |
E852793
|
entity |
| Predicate | hasNearDeathExperience |
P124566
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [TK Strand, hasNearDeathExperience, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearDeathExperience Context triple: [TK Strand, hasNearDeathExperience, true]
-
A.
nearDeathExperience
chosen
Indicates that an entity has undergone a profound experience in close proximity to death or a life-threatening situation.
-
B.
nearlyDiesFrom
Indicates that an entity comes very close to dying as a result of another entity or event, but ultimately survives.
-
C.
mayExperience
Indicates that an entity is capable of undergoing, feeling, or being subject to a particular event, state, or condition.
-
D.
hasMannerOfDeath
Indicates the specific way or circumstances in which an entity died, such as natural causes, accident, homicide, or suicide.
-
E.
hasFictionalAccident
Indicates that an entity experiences or is involved in an accident that occurs within a fictional or imagined context.
- 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_69f349679fd8819093b9b40e989440e3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff76ac40988190a34d858b5472ee2b |
completed | May 9, 2026, 6:02 p.m. |
| PD | Predicate disambiguation | batch_69ff760a90948190a12fcb80e6e3e14b |
completed | May 9, 2026, 5:59 p.m. |
Created at: May 1, 2026, 1:33 a.m.