Triple
T675158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heisei |
E13061
|
entity |
| Predicate | timeSpanInYears |
P302
|
FINISHED |
| Object | about 30 years |
—
|
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: about 30 years | Statement: [Heisei, timeSpanInYears, about 30 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timeSpanInYears Context triple: [Heisei, timeSpanInYears, about 30 years]
-
A.
countsYearsFrom
Indicates a temporal relationship where the number of years is measured starting from a specified reference point or event.
-
B.
timePeriod
chosen
Indicates the specific span or interval of time during which an event, state, or relationship occurs or is valid.
-
C.
serviceYears
Indicates the number of years an entity has provided service or been in a particular role, position, or organization.
-
D.
yearOfUse
Indicates the specific year during which something was in use or actively utilized.
-
E.
yearType
Indicates the classification or category assigned to a specific year (e.g., academic, fiscal, calendar, leap).
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0266e7c8190a94c4b4b761c59f4 |
completed | March 1, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69a49d1bbd0c81909cfbec30bd17bde7 |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.