Triple
T35010153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raven Rock |
E1009920
|
entity |
| Predicate | realWorldCounterpartLocation |
P123419
|
FINISHED |
| Object | Pennsylvania |
—
|
NE NERFINISHED |
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: Pennsylvania | Statement: [Raven Rock, realWorldCounterpartLocation, Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: realWorldCounterpartLocation Context triple: [Raven Rock, realWorldCounterpartLocation, Pennsylvania]
-
A.
portrayedByRealWorldLocation
Indicates that a fictional or represented location is depicted or substituted by an actual real-world location.
-
B.
counterpartLocatedAt
Indicates that one entity serves as the corresponding or matching counterpart of another entity at a specific location or site.
-
C.
associatedRealWorldTown
chosen
Indicates a relationship where an entity is linked or connected to a specific real-world town.
-
D.
realWorldName
Indicates that an entity’s name in a real-world context is given or associated with another value.
-
E.
characterRealWorldCounterpart
Indicates that a fictional character is based on, inspired by, or directly corresponds to a specific real-world person.
- 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_69f76dcc3ac8819096a3ed52f5fa2523 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7858aa5508190a07dde993b3356fc |
completed | May 3, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69f7841812f081909d878955d114088e |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.