Triple
T17693050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramsay Street |
E441082
|
entity |
| Predicate | hasFictionalHouseNumberRange |
P128601
|
FINISHED |
| Object | 22–32 |
—
|
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: 22–32 | Statement: [Ramsay Street, hasFictionalHouseNumberRange, 22–32]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalHouseNumberRange Context triple: [Ramsay Street, hasFictionalHouseNumberRange, 22–32]
-
A.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
-
B.
settingOfFictionalResidence
Indicates that a location serves as the setting or backdrop for a fictional residence within a narrative work.
-
C.
hasNumberOfHouses
Indicates the quantity of houses associated with a given entity.
-
D.
hasFictionalProprietor
Indicates that something is owned, managed, or run by a fictional character or entity within a narrative context.
-
E.
stateOfFictionalResidence
Indicates the state or region in which a fictional character’s residence is located.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47153a5c8819095c36fd414167fb1 |
completed | April 19, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:03 a.m.