Triple
T39034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William |
E772
|
entity |
| Predicate | historicalPopularity |
P1755
|
FINISHED |
| Object | medieval England |
—
|
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: medieval England | Statement: [William, historicalPopularity, medieval England]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalPopularity Context triple: [William, historicalPopularity, medieval England]
-
A.
popularInCentury
Indicates that something was widely liked, influential, or commonly recognized during a specified century.
-
B.
popularity
chosen
Indicates how widely liked, admired, or favored something or someone is by a group of people.
-
C.
notableEra
Indicates the historical period or era for which an entity is especially recognized or significant.
-
D.
historicalAssessment
Indicates an evaluation or judgment of something based on its historical context, significance, or development over time.
-
E.
historicalOutcome
Indicates the result or consequence that an event, action, or situation produced in a historical context.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24b4d5bd08190a3a48eb26e67768c |
completed | Feb. 28, 2026, 1:56 a.m. |
| PD | Predicate disambiguation | batch_69a24ab6141881908701106aa97e4735 |
completed | Feb. 28, 2026, 1:53 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.