Triple
T29228044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skyfall estate |
E740986
|
entity |
| Predicate | fictionalNearestCity |
P47231
|
FINISHED |
| Object | Glencoe area (implied) |
—
|
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: Glencoe area (implied) | Statement: [Skyfall estate, fictionalNearestCity, Glencoe area (implied)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalNearestCity Context triple: [Skyfall estate, fictionalNearestCity, Glencoe area (implied)]
-
A.
locatedNearFiction
chosen
Indicates that one fictional entity or place is situated close to another within an imagined or narrative context.
-
B.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
C.
nearestCityTo
Indicates that one city is the closest in distance to a given location or entity compared to all other cities.
-
D.
neighborOfFictional
Indicates that one fictional entity is located next to or in close proximity to another fictional entity within a narrative or imagined setting.
-
E.
neighborhoodOfFictionalSetting
Indicates that one fictional setting is a neighborhood or local area within another fictional setting.
- 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_69f07cbb12bc81908c1971d9de9a8d2a |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69fcab6e888881908ca9e18660928a40 |
completed | May 7, 2026, 3:10 p.m. |
| PD | Predicate disambiguation | batch_69fc4562a5b88190bad48f083a6dcdfa |
completed | May 7, 2026, 7:55 a.m. |
Created at: April 28, 2026, 12:17 p.m.