Triple
T24862787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baxter Springs Massacre |
E622196
|
entity |
| Predicate | killedUnion |
P125463
|
FINISHED |
| Object | dozens of Union soldiers and civilians |
—
|
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: dozens of Union soldiers and civilians | Statement: [Baxter Springs Massacre, killedUnion, dozens of Union soldiers and civilians]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: killedUnion Context triple: [Baxter Springs Massacre, killedUnion, dozens of Union soldiers and civilians]
-
A.
killedBy
Indicates that one entity caused the death of another entity.
-
B.
killedAlongWith
Indicates that one entity was killed at the same time and in the same event or circumstance as another entity.
-
C.
casualtiesUnionKilled
chosen
Indicates that the number of casualties consists of individuals who were killed and were members of a union.
-
D.
killedGroup
Indicates that one group caused the death of another group.
-
E.
killedDuring
Indicates that one entity caused the death of another entity in the course of, or as part of, a specified event or time period.
- 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_69e2fac350d08190b3affde1b451a8c5 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f44a417a58819081777e18dda149fd |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442b8479c8190a7c8e416ac9e28a0 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 5:22 a.m.