Triple
T751070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weeping Angels |
E15447
|
entity |
| Predicate | notableVictims |
P19083
|
FINISHED |
| Object | Sally Sparrow's acquaintances |
—
|
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: Sally Sparrow's acquaintances | Statement: [Weeping Angels, notableVictims, Sally Sparrow's acquaintances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableVictims Context triple: [Weeping Angels, notableVictims, Sally Sparrow's acquaintances]
-
A.
notableVictim
Indicates that the subject is a person or entity who is notably recognized as a victim of the object (such as an event, crime, or harmful action).
-
B.
numberOfGermanVictims
Indicates the quantity of victims who are identified as German in the context of the described event or situation.
-
C.
numberOfJewsKilled
Indicates the quantity of Jewish people who were killed in a given event, context, or time period.
-
D.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
E.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
- 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_69a493599a0081908da65f3407af1ef2 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a64c67748190aef3522b4b428563 |
completed | March 1, 2026, 8:49 p.m. |
| PD | Predicate disambiguation | batch_69a4a501c4cc81908de6d63e3d4f60d7 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5bed20c81909ecc28bf42594e72 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.