Triple
T14499813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhu Yousong |
E359602
|
entity |
| Predicate | regnalYearCount |
P114475
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Zhu Yousong, regnalYearCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regnalYearCount Context triple: [Zhu Yousong, regnalYearCount, 2]
-
A.
countsYearsFrom
Indicates a temporal relationship where the number of years is measured starting from a specified reference point or event.
-
B.
nationalYears
Indicates the span of years during which an entity was active or affiliated at the national level (e.g., on a national team or in a national role).
-
C.
totalCommonYears
Indicates the total number of years that two or more entities have in common, such as overlapping durations or shared time periods.
-
D.
yearPassed
Indicates that a specified number of calendar years has elapsed between two time points or events.
-
E.
hasNumberInYear
Indicates that a specific number is associated with or occurs within a given 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de94dfe484819086dd971606e6478e |
completed | April 14, 2026, 7:26 p.m. |
| PD | Predicate disambiguation | batch_69de5c4ccba08190a988bfda0bc9f5cb |
completed | April 14, 2026, 3:25 p.m. |
| PDg | Predicate description generation | batch_69de5fb4de14819092acdecbd201d672 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:21 a.m.