Triple
T3711057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | hitsumabushi |
E81410
|
entity |
| Predicate | servingStyleDifferenceFromUnadon |
P14779
|
FINISHED |
| Object | served in small pieces and mixed with rice |
—
|
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: served in small pieces and mixed with rice | Statement: [hitsumabushi, servingStyleDifferenceFromUnadon, served in small pieces and mixed with rice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servingStyleDifferenceFromUnadon Context triple: [hitsumabushi, servingStyleDifferenceFromUnadon, served in small pieces and mixed with rice]
-
A.
servingStyle
chosen
Indicates how something (typically food or drink) is presented or offered for consumption or use.
-
B.
diningStyle
Indicates the manner or format in which dining is conducted, such as casual, formal, buffet, or family-style.
-
C.
servesDish
Indicates that one entity prepares and presents a specific dish as food for another entity.
-
D.
servesMode
Indicates that one entity provides or operates in a particular manner, method, or mode in relation to another entity or context.
-
E.
servesMostly
Indicates that one entity primarily functions to serve, support, or cater to another entity, more than to any other.
- 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_69ad8b1a81588190b3f27a5483bb610e |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc584b86c8190ba1a1073da440b07 |
completed | March 8, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69adc041a8608190a2d543dab6d2ef6c |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:33 p.m.