Triple
T26876082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicolas Charles Oudinot |
E676751
|
entity |
| Predicate | numberOfWoundsReported |
P82703
|
FINISHED |
| Object | more than thirty |
—
|
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: more than thirty | Statement: [Nicolas Charles Oudinot, numberOfWoundsReported, more than thirty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWoundsReported Context triple: [Nicolas Charles Oudinot, numberOfWoundsReported, more than thirty]
-
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.
casualtiesWounded
Indicates that an event or situation resulted in people being injured but not killed.
-
D.
woundedAt
Indicates that an entity was injured or harmed at a specific place or during a particular event.
-
E.
wasWoundedIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or conflict.
- 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_69eee9bb44988190b6e11652d028bc59 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69fe5ec9028081909ae3d6fbe2f4cbbc |
completed | May 8, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69fe5e1d715881909fc516fafc707644 |
completed | May 8, 2026, 10:05 p.m. |
Created at: April 27, 2026, 5:36 a.m.