Triple
T11186167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Cap-Français |
E264672
|
entity |
| Predicate | hasPartInYear |
P97750
|
FINISHED |
| Object | 1793 in the Haitian Revolution |
—
|
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: 1793 in the Haitian Revolution | Statement: [Siege of Cap-Français, hasPartInYear, 1793 in the Haitian Revolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPartInYear Context triple: [Siege of Cap-Français, hasPartInYear, 1793 in the Haitian Revolution]
-
A.
hasAssociatedYear
Indicates that an entity is linked to a specific year that is relevant to it (e.g., creation, occurrence, or reference year).
-
B.
hasTypeOfYear
Indicates that a given year is classified as belonging to a specific type or category of year (e.g., fiscal, academic, leap).
-
C.
hasNumberInYear
Indicates that a specific number is associated with or occurs within a given year.
-
D.
foundInYear
Indicates the specific calendar year in which something was discovered, created, established, or otherwise found.
-
E.
mappedInYear
Indicates that something was mapped or charted during a specific calendar year.
- 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_69d6aa9eb9248190b20211772621b4bc |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8abbeac8190ad6e419258999f4e |
completed | April 9, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69d75cf4461c8190af84060f7db83211 |
completed | April 9, 2026, 8:01 a.m. |
| PDg | Predicate description generation | batch_69d77062271c8190b63da714ab5beff9 |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 8, 2026, 9:29 p.m.