Triple
T11039874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | black laced Cochin |
E260982
|
entity |
| Predicate | hasCarriage |
P97438
|
FINISHED |
| Object | low and rounded |
—
|
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: low and rounded | Statement: [black laced Cochin, hasCarriage, low and rounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCarriage Context triple: [black laced Cochin, hasCarriage, low and rounded]
-
A.
carriageType
Indicates the specific kind or category of carriage associated with or used in relation to an entity.
-
B.
hasCabType
Indicates that an entity is associated with or characterized by a specific type or category of cab.
-
C.
carriedByLine
Indicates that something (such as a service, signal, or connection) is transported or conveyed via a particular line or conduit.
-
D.
hasCarriagewayType
Indicates the specific structural or functional type of carriageway associated with a road segment (e.g., single, dual, or other carriageway configurations).
-
E.
hasCross
Indicates that one entity possesses, displays, or is marked by a cross in relation to another entity or context.
- F. None of above. chosen
Provenance (4 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797ff519481909ebc2515b3d241de |
completed | April 9, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69d74407cb088190ba37c8da3d342b64 |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750c99f9881908ee2b01b6ce4b3a1 |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:26 p.m.