Triple
T34969380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annorax |
E1008490
|
entity |
| Predicate | temporalCrime |
P182151
|
FINISHED |
| Object | erasing entire species from 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: erasing entire species from history | Statement: [Annorax, temporalCrime, erasing entire species from history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temporalCrime Context triple: [Annorax, temporalCrime, erasing entire species from history]
-
A.
timeframeOfCrimes
Indicates the period or span of time during which the crimes occurred or were committed.
-
B.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
C.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
D.
crimeAgainst
Indicates that one entity commits or is responsible for a criminal act directed toward another entity.
-
E.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
- F. None of above. chosen
Provenance (4 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_69f76dc78a308190a1ac29ad4a9a4895 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f7870dfe108190996c0c68630edc7f |
completed | May 3, 2026, 5:34 p.m. |
Created at: May 3, 2026, 4 p.m.