Triple
T23237131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chillicothe, Missouri |
E581332
|
entity |
| Predicate | hasBreadSlicingEventYear |
P102571
|
FINISHED |
| Object | 1928 |
—
|
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: 1928 | Statement: [Chillicothe, Missouri, hasBreadSlicingEventYear, 1928]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBreadSlicingEventYear Context triple: [Chillicothe, Missouri, hasBreadSlicingEventYear, 1928]
-
A.
hasSpecialYear
Indicates that an entity is associated with a particular year that is distinguished or treated as exceptional in some defined context.
-
B.
hasPartInYear
Indicates that something includes or contains a specific part, component, or segment that is associated with a particular year.
-
C.
hasHistoricEventYear
chosen
Indicates the specific year in which a referenced historic event took place.
-
D.
eatenOnOccasion
Indicates that one entity is consumed or eaten by another only at certain times or under specific circumstances, rather than regularly or habitually.
-
E.
banYear
Indicates the year in which a ban was enacted or came into effect on the related entity or activity.
- 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_69e2460556f88190be1744a84a84173f |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f192ea590c81908cd677d89f67d49f |
completed | April 29, 2026, 5:11 a.m. |
| PD | Predicate disambiguation | batch_69effcdadec0819092ec1749ee453b4e |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:09 p.m.