Triple
T13386884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago bungalows |
E319466
|
entity |
| Predicate | typicalNumberOfStories |
P995
|
FINISHED |
| Object | one-and-a-half stories |
—
|
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: one-and-a-half stories | Statement: [Chicago bungalows, typicalNumberOfStories, one-and-a-half stories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfStories Context triple: [Chicago bungalows, typicalNumberOfStories, one-and-a-half stories]
-
A.
numberOfStories
chosen
Indicates the total count of levels or floors that a structure or building has.
-
B.
talesCount
Indicates the number of tales associated with or attributed to a given entity.
-
C.
storyNumber
Indicates the numerical identifier assigned to a specific story within a collection, sequence, or dataset.
-
D.
sectionCountApproximate
Indicates that the number of sections associated with an entity is known only approximately rather than as an exact count.
-
E.
timePeriodOfPrimaryStories
Indicates the time period during which the primary stories or main narrative events of something (e.g., a work or series) take place.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadce96d1881909957fdd068a7f55d |
completed | April 11, 2026, 11:44 p.m. |
| PD | Predicate disambiguation | batch_69d9a03189908190a784a2755f8d81e1 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:34 p.m.