Triple
T9819771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sacramento Tower Bridge |
E238498
|
entity |
| Predicate | repaintedColor |
P90177
|
FINISHED |
| Object | gold in 2002 |
—
|
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: gold in 2002 | Statement: [Sacramento Tower Bridge, repaintedColor, gold in 2002]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: repaintedColor Context triple: [Sacramento Tower Bridge, repaintedColor, gold in 2002]
-
A.
paintedIn
Indicates that an artwork or object was created or executed using paint within a specific time period or at a particular location.
-
B.
capeColor
Indicates the color attribute associated with a cape worn or possessed by an entity.
-
C.
paintedEvery
Indicates that an entity applied paint to each and every relevant item in a specified set or domain.
-
D.
paintedOn
Indicates that one entity has been applied as paint onto the surface of another entity.
-
E.
pasteColor
Indicates that one entity applies or transfers a color from a source to a target, effectively pasting that color onto the target.
- 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_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb2f74e348190be8e4394ae6fe3fe |
completed | April 2, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69cd03e01ea881909a7d93fc3994ace5 |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06abc9248190a506b64e9c516d03 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:31 p.m.