Triple
T25172803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul City Tower (unbuilt) |
E630365
|
entity |
| Predicate | involvesProgramType |
P2192
|
FINISHED |
| Object | office tower (proposed) |
—
|
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: office tower (proposed) | Statement: [Seoul City Tower (unbuilt), involvesProgramType, office tower (proposed)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesProgramType Context triple: [Seoul City Tower (unbuilt), involvesProgramType, office tower (proposed)]
-
A.
containsProgramType
Indicates that one entity includes or encompasses a specific type or category of program.
-
B.
programType
chosen
Indicates the category or kind of program to which an entity belongs or with which it is associated.
-
C.
relatesToProgram
Indicates that one entity has a relevant connection, association, or involvement with a particular program.
-
D.
intendedProgram
Indicates the academic or training program that an entity plans or expects to pursue, rather than one they are currently enrolled in or have completed.
-
E.
worksOnProgram
Indicates that an entity is actively involved in contributing effort or performing tasks on a particular program.
- 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_69e75a87c9b88190ab60731902a99750 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f657f653448190a945b4751af8507d |
completed | May 2, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69f6575ba12081909396036f78757a76 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 21, 2026, 12:21 p.m.