Triple
T675140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heisei |
E13061
|
entity |
| Predicate | eraNumberInJapan |
P17918
|
FINISHED |
| Object | 247th era name (traditional count) |
—
|
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: 247th era name (traditional count) | Statement: [Heisei, eraNumberInJapan, 247th era name (traditional count)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eraNumberInJapan Context triple: [Heisei, eraNumberInJapan, 247th era name (traditional count)]
-
A.
eraNameInJapanese
Indicates the Japanese-language name used for a specific historical or calendar era.
-
B.
nameInJapaneseKana
Indicates that an entity’s name is written or represented using Japanese kana characters.
-
C.
gdpRankInJapan
Indicates the position of an entity in the ordered ranking of GDP values within Japan.
-
D.
usesKatakanaFor
Indicates that one entity is written or represented using katakana script in relation to another entity.
-
E.
hasOfficialNameInJapanese
Indicates that an entity has an official, formally recognized name expressed in the Japanese language.
- 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_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. |
| PDg | Predicate description generation | batch_69a49ebf33c481909949526cb8f223dd |
completed | March 1, 2026, 8:17 p.m. |
Created at: March 1, 2026, 7:36 p.m.