Triple
T30070688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Rizzio |
E764173
|
entity |
| Predicate | numberOfStabWounds |
P82703
|
FINISHED |
| Object | over 50 |
—
|
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: over 50 | Statement: [David Rizzio, numberOfStabWounds, over 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStabWounds Context triple: [David Rizzio, numberOfStabWounds, over 50]
-
A.
hasApproximateNumberOfWounds
chosen
Indicates that an entity has a number of wounds that is known only approximately rather than as an exact count.
-
B.
numberOfGunshotWounds
Indicates the count of gunshot wounds associated with a particular entity or event.
-
C.
woundedAt
Indicates that an entity was injured or harmed at a specific place or during a particular event.
-
D.
firstWoundInflictedBy
Indicates that the referenced wound is the earliest (chronologically first) injury inflicted by the specified agent on the specified target.
-
E.
woundedSeverely
Indicates that one entity has inflicted or suffered a level of injury on another that is serious, potentially life-threatening, or causes significant impairment.
- 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_69f2247221388190a13a22c47094a0ef |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a003ffb548081908ba64fd2b570f42f |
completed | May 10, 2026, 8:21 a.m. |
| PD | Predicate disambiguation | batch_6a003fcecf788190a13dff38b9de3414 |
completed | May 10, 2026, 8:20 a.m. |
Created at: April 29, 2026, 7 p.m.