Triple
T34711548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Narciso |
E1000658
|
entity |
| Predicate | containsFictionalNeighborhoodOrArea |
P174424
|
FINISHED |
| Object | Echo Courts |
—
|
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: Echo Courts | Statement: [San Narciso, containsFictionalNeighborhoodOrArea, Echo Courts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsFictionalNeighborhoodOrArea Context triple: [San Narciso, containsFictionalNeighborhoodOrArea, Echo Courts]
-
A.
neighborhoodOfFictionalSetting
chosen
Indicates that one fictional setting is a neighborhood or local area within another fictional setting.
-
B.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
C.
hasFictionalCityContext
Indicates that something is associated with, set in, or contextualized by a fictional city.
-
D.
hasFictionalLocation
Indicates that an entity is associated with, set in, or takes place within a location that exists only in fiction rather than in the real world.
-
E.
hasBranchInFictionalLocation
Indicates that an organization maintains a branch, office, or presence within a fictional or imaginary location.
- 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_69f76dad3f108190a280fd0a2f4ee89a |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_6a00818b20a881909fbf3bb33dcf7029 |
completed | May 10, 2026, 1 p.m. |
| PD | Predicate disambiguation | batch_6a0080f76f588190a933238861243d1a |
completed | May 10, 2026, 12:58 p.m. |
Created at: May 3, 2026, 3:59 p.m.