Triple
T7384396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taiping Heavenly Kingdom |
E170343
|
entity |
| Predicate | deathTollContext |
P700
|
FINISHED |
| Object | one of the deadliest conflicts in history |
—
|
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: one of the deadliest conflicts in history | Statement: [Taiping Heavenly Kingdom, deathTollContext, one of the deadliest conflicts in history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deathTollContext Context triple: [Taiping Heavenly Kingdom, deathTollContext, one of the deadliest conflicts in history]
-
A.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
B.
deathTollEstimate
chosen
Indicates an estimated number of deaths attributed to a particular event, cause, or period.
-
C.
deathApprox
Indicates that an entity’s death occurred at an approximate, rather than exact, time or date.
-
D.
deathContributedTo
Indicates that one entity played a causal or contributing role in bringing about the death of another entity.
-
E.
deathDescribedIn
Indicates that a person's death is documented, narrated, or otherwise detailed within a particular source or description.
- 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_69c68a5d0ed08190b6d361e68f813330 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1efe1308190b96eefbff56140be |
completed | March 27, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c6f0309cc88190b55d278969400294 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:08 p.m.