Triple
T25295677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ernie Cumberbatch |
E634210
|
entity |
| Predicate | houseAddressInFiction |
P135969
|
FINISHED |
| Object | 704 Hauser Street |
—
|
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: 704 Hauser Street | Statement: [Ernie Cumberbatch, houseAddressInFiction, 704 Hauser Street]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: houseAddressInFiction Context triple: [Ernie Cumberbatch, houseAddressInFiction, 704 Hauser Street]
-
A.
settingOfFictionalResidence
Indicates that a location serves as the setting or backdrop for a fictional residence within a narrative work.
-
B.
stateOfFictionalResidence
Indicates the state or region in which a fictional character’s residence is located.
-
C.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
-
D.
hasFictionalAddressTown
chosen
Indicates that an entity is associated with a town that serves as its fictional address location.
-
E.
hasFictionalHouseNumberRange
Indicates that an entity is associated with a range of house numbers that are fictional or not used in real-world addressing.
- 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_69e75a9503d48190b80a005c6af0cb50 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f497bc12b881908fe3386c66252bf6 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f49377411c8190b2188de444d76795 |
completed | May 1, 2026, 11:50 a.m. |
Created at: April 21, 2026, 1:22 p.m.