Triple
T22960448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ringwood |
E570879
|
entity |
| Predicate | hasTraditionalTownCentre |
P121519
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Ringwood, hasTraditionalTownCentre, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalTownCentre Context triple: [Ringwood, hasTraditionalTownCentre, true]
-
A.
hasCentralTownFeature
chosen
Indicates that a town possesses a specific central feature or focal element (such as a landmark, square, or facility) that characterizes its core area.
-
B.
containsTraditionalCity
Indicates that one entity geographically or administratively includes or encompasses a traditional city within its boundaries.
-
C.
hasTraditionalCommunity
Indicates that an entity possesses or is associated with a community that maintains traditional practices, customs, or ways of life.
-
D.
hasTraditionalSettlementType
Indicates that an entity is associated with a specific traditional or historically established type of human settlement (e.g., village, town, hamlet).
-
E.
hasCommercialCenterType
Indicates that an entity has or is associated with a specific type or category of commercial center (e.g., mall, shopping district, business park).
- 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f3c96081909abd6ec32103d4c3 |
completed | April 29, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:47 p.m.