Triple
T13763332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 500 series Shinkansen |
E330669
|
entity |
| Predicate | setNumbers |
P110862
|
FINISHED |
| Object | W1–W9 |
—
|
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: W1–W9 | Statement: [500 series Shinkansen, setNumbers, W1–W9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setNumbers Context triple: [500 series Shinkansen, setNumbers, W1–W9]
-
A.
setsNumberOf
Indicates that one entity assigns or defines the numerical quantity or count associated with another entity.
-
B.
setsOut
Indicates that an entity begins a journey, course of action, or process, moving from an initial state or location toward a goal or destination.
-
C.
sampleNumber
Indicates that an entity is identified or associated with a specific sample number within a set of samples.
-
D.
sets
Indicates that an entity places, positions, or puts another entity into a particular state, location, or configuration.
-
E.
set
Indicates that an entity places, positions, or establishes another entity into a particular state, configuration, or location.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de022690ac8190bd5410ecc659a2a7 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe97846c819093b00ea117b64e0d |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc59db0148190bcaf9646403ca64f |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 9, 2026, 10:10 p.m.