Triple
T3787596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Common Era |
E85564
|
entity |
| Predicate | sharesYearNumberingWith |
P51527
|
FINISHED |
| Object | AD system |
—
|
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: AD system | Statement: [Common Era, sharesYearNumberingWith, AD system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesYearNumberingWith Context triple: [Common Era, sharesYearNumberingWith, AD system]
-
A.
divisionYear
Indicates the year in which a division or split of an entity took place.
-
B.
sharesHistoryWith
Indicates that two entities have a common or overlapping past, such as shared experiences, events, or origins.
-
C.
hasNumberInYear
Indicates that a specific number is associated with or occurs within a given year.
-
D.
sharesBaseWith
Indicates that two entities have a common underlying base element, source, or component from which they are derived or constructed.
-
E.
totalCommonYears
Indicates the total number of years that two or more entities have in common, such as overlapping durations or shared time periods.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee634c6ac819099653c660c286746 |
completed | March 9, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69aee3d3c92c819081d9d5c45ef37a5d |
completed | March 9, 2026, 3:14 p.m. |
| PDg | Predicate description generation | batch_69aee633dab88190b14cec8afb19ca6a |
completed | March 9, 2026, 3:24 p.m. |
Created at: March 9, 2026, 3:13 p.m.