Triple
T26369042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Xiaoting |
E660720
|
entity |
| Predicate | impactOnShu |
P102027
|
FINISHED |
| Object | severe loss of troops and generals |
—
|
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: severe loss of troops and generals | Statement: [Battle of Xiaoting, impactOnShu, severe loss of troops and generals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnShu Context triple: [Battle of Xiaoting, impactOnShu, severe loss of troops and generals]
-
A.
impactOnSubject
chosen
Indicates the effect, influence, or consequence that one entity, event, or action has on a specified subject.
-
B.
impactStatus
Indicates the current state or condition of how something has affected or influenced a target.
-
C.
recognizesImpactOn
Indicates that one entity acknowledges or understands the effect or consequences it has on another entity or situation.
-
D.
impactDescription
Indicates a description of the effect, consequence, or influence that one entity, action, or event has on another.
-
E.
impactBuilding
Indicates that one entity physically collides with or strikes a building, causing an impact event.
- 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_69ee812a698881908d6a58265995fa39 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 26, 2026, 10:57 p.m.