Triple
T12289961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Immersion Land |
E292929
|
entity |
| Predicate | hasPhysicalLocationType |
P104071
|
FINISHED |
| Object | underground transit station |
—
|
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: underground transit station | Statement: [Immersion Land, hasPhysicalLocationType, underground transit station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhysicalLocationType Context triple: [Immersion Land, hasPhysicalLocationType, underground transit station]
-
A.
hasMainLocationType
Indicates that an entity is associated with a primary or predominant type of location that characterizes where it is mainly situated or operates.
-
B.
hasPrimaryAssetLocation
Indicates that an entity’s main or principal asset is located at a specified place or facility.
-
C.
hasLocationRole
Indicates that an entity holds or plays a specific role in relation to a particular location (e.g., origin, destination, storage site, or operational area).
-
D.
hasStoreLocation
Indicates that an entity operates or maintains a store at a specified physical location.
-
E.
isPhysicalArtifact
Indicates that the subject is a tangible, man-made object that physically exists in the real world.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9261e1570819084bb4fdb44aa6aea |
completed | April 10, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69d91c4d9a9c8190aeb7beaf9792d8f0 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d9261b7f088190b69fe6961015fce3 |
completed | April 10, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:52 p.m.