Triple
T34427180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edward Atterton as King Arthur |
E883714
|
entity |
| Predicate | associatedWithPlaceInStory |
P84403
|
FINISHED |
| Object | Camelot |
E1555931
|
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: Camelot | Statement: [Edward Atterton as King Arthur, associatedWithPlaceInStory, Camelot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithPlaceInStory Context triple: [Edward Atterton as King Arthur, associatedWithPlaceInStory, Camelot]
-
A.
associatedWithPersonInStory
Indicates that one entity has a connection or involvement with a specific person within the context of a story.
-
B.
fictionalLocationAssociatedWith
chosen
Indicates a relationship where a fictional entity (such as a character, event, or work) is connected to or set in a particular fictional location.
-
C.
associatedTale
Indicates that one entity is linked or connected to a particular tale, story, or narrative.
-
D.
associatedWithFictionalEvent
Indicates that an entity has a connection or involvement with a fictional event, such as being based on, inspired by, or participating in that imagined occurrence.
-
E.
narrativeLocationType
Indicates the type or role of a location within the structure or context of a narrative (e.g., setting, origin, destination).
- 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_69f349c3dd2c819092cc9e64809f4a42 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a037c8ae0248190b7e2ce4bf852c22d |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a370ddd9dd8819090ce73a4ee236075 |
completed | June 20, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_6a0379fbe4a08190bfe65ebd141164e9 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 2 a.m.