Triple
T329956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Museo Arqueológico de La Serena |
E6603
|
entity |
| Predicate | mainBuildingFunction |
P1844
|
FINISHED |
| Object | exhibition of archaeological heritage |
—
|
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: exhibition of archaeological heritage | Statement: [Museo Arqueológico de La Serena, mainBuildingFunction, exhibition of archaeological heritage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainBuildingFunction Context triple: [Museo Arqueológico de La Serena, mainBuildingFunction, exhibition of archaeological heritage]
-
A.
mainBuildingName
Indicates the name that is designated as the primary or main building associated with an entity.
-
B.
building
Indicates that one entity constructs, assembles, or develops another entity, typically over a period of time.
-
C.
mainBase
Indicates that one entity serves as the primary or central base or headquarters for another entity.
-
D.
buildingSection
Indicates a relationship where one entity is a specific section, part, or subdivision of a larger building.
-
E.
buildingType
chosen
Indicates the specific category or function that characterizes what kind of building something is.
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eaaeb64881909c7ab9bca3378e2b |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94aab1c8190b8654708c87eeb91 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.