Triple
T1226519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madrid Metro |
E26338
|
entity |
| Predicate | hasSurfaceSections |
P24720
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Madrid Metro, hasSurfaceSections, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSurfaceSections Context triple: [Madrid Metro, hasSurfaceSections, yes]
-
A.
hasSect
Indicates that an entity includes, contains, or is associated with a particular sect or subgroup within a larger religious, ideological, or organizational context.
-
B.
hasSectionCount
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
C.
hasBridgeSection
Indicates that one entity includes or is associated with a specific bridge section as a distinct part or component.
-
D.
hasLowLevelSection
Indicates that an entity includes or is associated with a subordinate or lower-level section within a larger hierarchical structure.
-
E.
hasTreeLinedSections
Indicates that portions of an entity (such as a route, street, or path) are bordered or lined with trees along their length.
- 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_69a49484688c8190a1bf285eb396a8b6 |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4be39908481908cca21aaf0828415 |
completed | March 1, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69a4bb65d61c8190bf0424ea0019a98b |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bbf83584819088c69366f58586cc |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:47 p.m.