Triple
T670009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knights Hospitaller |
E12949
|
entity |
| Predicate | grantedTerritoryYear |
P18143
|
FINISHED |
| Object | 1530 |
—
|
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: 1530 | Statement: [Knights Hospitaller, grantedTerritoryYear, 1530]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grantedTerritoryYear Context triple: [Knights Hospitaller, grantedTerritoryYear, 1530]
-
A.
authorizationYear
Indicates the year in which an official approval, permission, or authorization for something was granted.
-
B.
claimedYear
Indicates the year that is asserted or reported as being associated with an event, status, or fact, regardless of whether it is verified.
-
C.
dateGranted
Indicates the specific date on which a right, status, or permission was formally conferred or approved.
-
D.
standardizedInYear
Indicates the specific year in which something was formally standardized or adopted as a standard.
-
E.
citizenshipGrantedYear
Indicates the specific year in which an entity was officially granted citizenship.
- 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49ffd2b508190ac5adc04163e360f |
completed | March 1, 2026, 8:22 p.m. |
| PD | Predicate disambiguation | batch_69a49d18942c819083b3d1887e505900 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a49fcf0cb4819096edea4037ca2c03 |
completed | March 1, 2026, 8:21 p.m. |
Created at: March 1, 2026, 7:36 p.m.