Triple
T9096834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loretta Brown |
E218045
|
entity |
| Predicate | causeOfDeathInSeries |
P144
|
FINISHED |
| Object | fall from a stool in a bathtub |
—
|
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: fall from a stool in a bathtub | Statement: [Loretta Brown, causeOfDeathInSeries, fall from a stool in a bathtub]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfDeathInSeries Context triple: [Loretta Brown, causeOfDeathInSeries, fall from a stool in a bathtub]
-
A.
causeOfDeath
chosen
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
B.
reasonForDeath
Indicates the cause, circumstance, or condition that led to an entity’s death.
-
C.
episodeOfDeath
Indicates the specific event or episode during which an entity’s death occurred.
-
D.
hasDiedInSeries
Indicates that an entity has died at some point within the narrative of a particular series.
-
E.
deathInFiction
Indicates that an entity’s death occurs within a fictional work or narrative rather than in real life.
- 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_69ca83d9844081908e561e367fda6d45 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc96b7d0d48190a3b15f35bef087e3 |
completed | April 1, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69cc65fc7f408190a5846e29ab3b97e5 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:15 p.m.