Triple
T3084198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheers |
E64329
|
entity |
| Predicate | locatedInFictionalAddress |
P14481
|
FINISHED |
| Object | 84 Beacon Street, Boston |
—
|
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: 84 Beacon Street, Boston | Statement: [Cheers, locatedInFictionalAddress, 84 Beacon Street, Boston]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInFictionalAddress Context triple: [Cheers, locatedInFictionalAddress, 84 Beacon Street, Boston]
-
A.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
B.
fictionalResidence
Indicates that one entity is the place where another entity lives or is based within a fictional or imaginary context.
-
C.
setInFictionalLocation
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
D.
fictionalAddress
chosen
Indicates that an address associated with an entity is invented or not corresponding to a real-world location.
-
E.
fictionalHeadquartersLocation
Indicates the place where a fictional organization, group, or entity is based or has its main headquarters within a fictional context.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e98a1c8190b1dd4a0a47f7d6c6 |
completed | March 8, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69ad9debb6308190be28378ae1fc98af |
completed | March 8, 2026, 4:03 p.m. |
Created at: March 8, 2026, 3:03 p.m.