Triple
T19727139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculty of Engineering (Osaka University) |
E473755
|
entity |
| Predicate | rankWithinJapan |
P137097
|
FINISHED |
| Object | among leading engineering faculties in Japan |
—
|
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: among leading engineering faculties in Japan | Statement: [Faculty of Engineering (Osaka University), rankWithinJapan, among leading engineering faculties in Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankWithinJapan Context triple: [Faculty of Engineering (Osaka University), rankWithinJapan, among leading engineering faculties in Japan]
-
A.
rankInJapaneseOrders
Indicates the position or level an entity holds within the hierarchy of Japanese orders, decorations, or honors.
-
B.
rankingByHeightInJapan
Indicates the relative order of entities based on their height specifically within the context of Japan.
-
C.
rankByCommonnessInJapan
Indicates how items are ordered based on how commonly they occur or are found in Japan.
-
D.
gdpRankInJapan
Indicates the position of an entity in the ordered ranking of GDP values within Japan.
-
E.
ratingJapan
Indicates that an entity assigns or holds a rating specifically related to Japan (e.g., its products, services, or overall experience).
- 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_69d8e517ebd48190979ee76723bcfadf |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e649f95d5c8190858ba414ff3e95a8 |
completed | April 20, 2026, 3:44 p.m. |
| PD | Predicate disambiguation | batch_69e5304a7aac8190ac13f75f0c008e45 |
completed | April 19, 2026, 7:43 p.m. |
| PDg | Predicate description generation | batch_69e532bbedf081908d801600e2af94a7 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 1:47 p.m.