Triple
T28259083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | stigmata of Saint Francis of Assisi |
E712533
|
entity |
| Predicate | hasWoundType |
P23450
|
FINISHED |
| Object | nail wounds in hands |
—
|
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: nail wounds in hands | Statement: [stigmata of Saint Francis of Assisi, hasWoundType, nail wounds in hands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWoundType Context triple: [stigmata of Saint Francis of Assisi, hasWoundType, nail wounds in hands]
-
A.
hasApproximateNumberOfWounds
Indicates that an entity has a number of wounds that is known only approximately rather than as an exact count.
-
B.
hasTypeOfDamage
Indicates that an entity experiences or exhibits a specific kind or category of damage.
-
C.
woundedAt
Indicates that an entity was injured or harmed at a specific place or during a particular event.
-
D.
injuryType
chosen
Indicates the specific kind or category of injury associated with an entity or event.
-
E.
woundLocation
Indicates the specific anatomical site on an entity’s body where a wound is present or occurred.
- 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_69efb5207eb08190827e4c34048030b1 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f7817daf00819098936402e75ab0a6 |
completed | May 3, 2026, 5:10 p.m. |
| PD | Predicate disambiguation | batch_69f780fc5ed88190b7200ee5a29940af |
completed | May 3, 2026, 5:08 p.m. |
Created at: April 27, 2026, 11:10 p.m.