Triple
T6604828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 6 (Madrid Metro) |
E149086
|
entity |
| Predicate | usageLevel |
P68012
|
FINISHED |
| Object | heavily used |
—
|
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: heavily used | Statement: [Line 6 (Madrid Metro), usageLevel, heavily used]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usageLevel Context triple: [Line 6 (Madrid Metro), usageLevel, heavily used]
-
A.
usageType
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
B.
usageAmong
chosen
Indicates how frequently or in what manner something is used within a particular group, context, or population.
-
C.
automationLevel
Indicates the degree to which a process, task, or system is performed automatically rather than manually.
-
D.
gradeUsage
Indicates how a particular grade or rating is applied or used in a given context.
-
E.
exportLevel
Indicates the degree or extent to which something is produced in one place and sent out or made available to other places or markets.
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acfd17388190bd0bb8b2371e7df1 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:56 p.m.