Triple
T38123773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Little Accidents |
E952010
|
entity |
| Predicate | hasSurvivorCharacter |
P202393
|
FINISHED |
| Object | Amos Jenkins |
—
|
NE NERFINISHED |
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: Amos Jenkins | Statement: [Little Accidents, hasSurvivorCharacter, Amos Jenkins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurvivorCharacter Context triple: [Little Accidents, hasSurvivorCharacter, Amos Jenkins]
-
A.
hasSurvivors
Indicates that one or more entities continue to exist or remain alive after a particular event, condition, or incident.
-
B.
isSurvivorOf
Indicates that one entity has lived through, endured, or remained alive after a particular event, condition, or harmful circumstance involving another entity.
-
C.
hasSurvivorTerm
Indicates that an entity is associated with a term or label specifically used to describe survivors of an event, condition, or circumstance.
-
D.
hasRescueTeamCharacter
Indicates that an entity includes or is associated with a character who is part of a rescue team.
-
E.
hasMurderVictimCharacter
Indicates that an entity (such as a work of fiction or event) includes or involves a character who is the victim of a murder.
- 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_69f76f07734c8190814e937e12257a78 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a007899cadc8190a04edd503eaf6514 |
completed | May 10, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_6a0078493e088190b0c5047cbe75d304 |
completed | May 10, 2026, 12:21 p.m. |
| PDg | Predicate description generation | batch_6a00789927708190b031d3a9d5f4f68e |
completed | May 10, 2026, 12:22 p.m. |
Created at: May 3, 2026, 4:21 p.m.