Triple
T4538459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chiloé wooden churches |
E107467
|
entity |
| Predicate | numberOfComponentSites |
P19198
|
FINISHED |
| Object | 16 |
—
|
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: 16 | Statement: [Chiloé wooden churches, numberOfComponentSites, 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfComponentSites Context triple: [Chiloé wooden churches, numberOfComponentSites, 16]
-
A.
numberOfSites
Indicates the total count of distinct sites associated with or involved in the given entity or context.
-
B.
hasComponentCount
chosen
Indicates that an entity is associated with a specific number of components it contains or comprises.
-
C.
numberOfConstituents
Indicates the total count of individual components or members that make up a larger whole or group.
-
D.
containsSite
Indicates that one entity spatially or structurally includes another entity as a site or location within its bounds.
-
E.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57b8c4788190b35d110553013ff1 |
completed | March 20, 2026, 2:20 p.m. |
| PD | Predicate disambiguation | batch_69bd521edd00819099dfccaa65dddd61 |
completed | March 20, 2026, 1:56 p.m. |
Created at: March 20, 2026, 1:04 p.m.