Triple
T2820171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abbot Kinney |
E54386
|
entity |
| Predicate | hasPartInHisProject |
P41623
|
FINISHED |
| Object | canals in Venice, Los Angeles |
—
|
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: canals in Venice, Los Angeles | Statement: [Abbot Kinney, hasPartInHisProject, canals in Venice, Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPartInHisProject Context triple: [Abbot Kinney, hasPartInHisProject, canals in Venice, Los Angeles]
-
A.
hasProject
Indicates that an entity is associated with or responsible for a particular project.
-
B.
hasPartInHisDesign
chosen
Indicates that one entity is included as a component or element within the design or plan created by another entity.
-
C.
hasNotableProject
Indicates that an entity is associated with a project that is distinguished or recognized as significant in some way.
-
D.
hasPart
Indicates that one entity is a component, segment, or constituent part of another entity.
-
E.
workedUnder
Indicates that one entity was hierarchically subordinate to and performed work under the supervision or authority of another entity.
- 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_69ab49de0af08190b3da69683be1e728 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd08f2f481908c3da8a9c7a00552 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 9:59 p.m.