Triple
T3711119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | kishimen noodles |
E81411
|
entity |
| Predicate | hasThickness |
P9690
|
FINISHED |
| Object | thin compared to regular udon |
—
|
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: thin compared to regular udon | Statement: [kishimen noodles, hasThickness, thin compared to regular udon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThickness Context triple: [kishimen noodles, hasThickness, thin compared to regular udon]
-
A.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
B.
sideArmorThickness
Indicates the thickness of an object's armor specifically along its sides.
-
C.
armorThicknessMax
Indicates the maximum thickness of armor that an entity possesses or can withstand.
-
D.
hasCrustalThickness
Indicates the relationship in which an object or region possesses a specified thickness of its crust.
-
E.
hasHeight
Indicates that one entity possesses a specific vertical measurement or stature.
- 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_69adc58617bc8190bb712d1c90394215 |
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.