Triple
T7971559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wildrose Charcoal Kilns |
E185333
|
entity |
| Predicate | numberOfStructures |
P64710
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Wildrose Charcoal Kilns, numberOfStructures, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStructures Context triple: [Wildrose Charcoal Kilns, numberOfStructures, 10]
-
A.
hasStructureCount
chosen
Indicates the number of structures associated with or contained by a given entity.
-
B.
remainingStructuresUsedFor
Indicates that the remaining structures of an entity are utilized for a specified purpose or function.
-
C.
numberOfBuildings
Indicates the total count of buildings associated with a given entity or within a specified context.
-
D.
usedStructure
Indicates that one entity makes use of, relies on, or operates through a particular structure (physical, logical, or organizational) to perform its function or action.
-
E.
numberOfTowers
Indicates the quantity of towers associated with or contained by a given 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_69ca8297699481909b75a405f01e03af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3bd476108190988a75653a5c56d6 |
completed | March 31, 2026, 3:13 a.m. |
| PD | Predicate disambiguation | batch_69cb047a8e4c81909b79e0f0bf56440c |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:13 p.m.