Triple
T10546910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liz Sherman |
E248841
|
entity |
| Predicate | hasTrauma |
P41242
|
FINISHED |
| Object | accidentally killed her family with her powers as a child |
—
|
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: accidentally killed her family with her powers as a child | Statement: [Liz Sherman, hasTrauma, accidentally killed her family with her powers as a child]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrauma Context triple: [Liz Sherman, hasTrauma, accidentally killed her family with her powers as a child]
-
A.
trauma
chosen
Indicates that an entity has experienced a deeply distressing or harmful event or series of events that cause lasting psychological or emotional impact.
-
B.
traumaLevel
Indicates the degree or severity of trauma experienced or present in relation to an entity or event.
-
C.
hasInjuries
Indicates that an entity has sustained one or more physical or bodily injuries.
-
D.
hasInjuredPerson
Indicates that an entity has a person who has been harmed or injured associated with it.
-
E.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d52710869c81909b6db1a190825bad |
completed | April 7, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69d518fa0b4081909bffc936d78bd77b |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:33 p.m.