Triple
T12955743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | American Foursquare |
E310004
|
entity |
| Predicate | hasEntryFeature |
P80525
|
FINISHED |
| Object | single front door with sidelights |
—
|
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: single front door with sidelights | Statement: [American Foursquare, hasEntryFeature, single front door with sidelights]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEntryFeature Context triple: [American Foursquare, hasEntryFeature, single front door with sidelights]
-
A.
hasEntryOn
Indicates that one entity contains or includes an entry, record, or listing about another entity.
-
B.
hasEntryType
Indicates that something is associated with a specific category or type of entry within a system or dataset.
-
C.
hasEntryRequirement
Indicates that one entity specifies conditions or qualifications that must be met before another entity is allowed access, participation, or admission.
-
D.
hasFeatureID
Indicates that an entity is associated with a specific feature identified by a unique ID.
-
E.
hasPrimaryFeature
chosen
Indicates that an entity possesses a main or most characteristic feature that defines or distinguishes it.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 5:44 p.m.