Triple
T32010404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hope & Gloria |
E817383
|
entity |
| Predicate | locatedInFictionalNeighborhood |
P47688
|
FINISHED |
| Object | Pittsburgh |
E19280
|
NE 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: Pittsburgh | Statement: [Hope & Gloria, locatedInFictionalNeighborhood, Pittsburgh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInFictionalNeighborhood Context triple: [Hope & Gloria, locatedInFictionalNeighborhood, Pittsburgh]
-
A.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
B.
neighborhoodOfFictionalSetting
Indicates that one fictional setting is a neighborhood or local area within another fictional setting.
-
C.
residesInFictionalLocation
chosen
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
-
D.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
-
E.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
- F. None of above.
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_69f348f9e5d081908cc3f57c4942af52 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a0205c0bff481908238a382459b3a93 |
completed | May 11, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2f0bb840e88190b5f4519007a4c493 |
completed | June 14, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_6a0205143f20819087ee31576835be26 |
completed | May 11, 2026, 4:34 p.m. |
Created at: May 1, 2026, 12:15 a.m.