Triple
T37510110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sears Roebuck model homes |
E932193
|
entity |
| Predicate | numberOfModelsOffered |
P102817
|
FINISHED |
| Object | over 400 |
—
|
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: over 400 | Statement: [Sears Roebuck model homes, numberOfModelsOffered, over 400]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfModelsOffered Context triple: [Sears Roebuck model homes, numberOfModelsOffered, over 400]
-
A.
numberOfModels
chosen
Indicates the quantity or count of models associated with a given entity or context.
-
B.
offeredOnModelType
Indicates that something is made available or provided specifically for a particular model type.
-
C.
someModelsCoreCount
Indicates that there exists at least one model whose core count satisfies the specified condition or relation.
-
D.
hasModels
Indicates that an entity possesses, defines, or is associated with one or more models (such as conceptual, mathematical, or data models).
-
E.
hasModelSeries
Indicates a relationship where an item or product is associated with a specific model series it belongs to.
- 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_69f76ec5268481909ea01c73aeeefd42 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1553e08190bb7424c448cb1f33 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:17 p.m.