Triple
T35914460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "Phoenix" portrait type |
E1038705
|
entity |
| Predicate | hasLocationOfExamples |
P58841
|
FINISHED |
| Object | National Portrait Gallery, London |
—
|
NE NERFINISHED |
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: National Portrait Gallery, London | Statement: ["Phoenix" portrait type, hasLocationOfExamples, National Portrait Gallery, London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLocationOfExamples Context triple: ["Phoenix" portrait type, hasLocationOfExamples, National Portrait Gallery, London]
-
A.
locationOfExamples
chosen
Indicates that something serves as the place or context where examples of a particular type, concept, or item can be found.
-
B.
locationExample
Indicates that one entity serves as an example or illustrative instance of a particular location associated with another entity.
-
C.
hasExamplePlaceName
Indicates that an entity is associated with a specific place name used as an example.
-
D.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
E.
usedAsLocationIn
Indicates that something serves as the setting or place where another event, action, or situation occurs.
- 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff41645c548190b7cb4e53079b93ef |
completed | May 9, 2026, 2:15 p.m. |
| PD | Predicate disambiguation | batch_69ff410aa33c8190869ba769ac2a93ce |
completed | May 9, 2026, 2:13 p.m. |
Created at: May 3, 2026, 4:07 p.m.