Triple
T6819835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stadium of Epidaurus |
E156868
|
entity |
| Predicate | distanceFromTheater |
P73247
|
FINISHED |
| Object | approximately 500 meters |
—
|
LITERAL 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: approximately 500 meters | Statement: [Stadium of Epidaurus, distanceFromTheater, approximately 500 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromTheater Context triple: [Stadium of Epidaurus, distanceFromTheater, approximately 500 meters]
-
A.
distanceFromStation
Indicates the measured spatial separation between an entity and a specified station.
-
B.
distanceFromDowntown
Indicates the physical distance between a given location and the central downtown area.
-
C.
distancedFrom
Indicates that one entity is physically or metaphorically kept at a certain distance or separation from another entity.
-
D.
distanceToHollywood
Indicates the spatial distance between a given entity and Hollywood.
-
E.
proximityToLandmark
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
- F. None of above. chosen
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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d35781e88190a45d1386706d4422 |
completed | March 27, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_69c6d09bb4f881909bf20c188cb3e8e1 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d1d5f1908190989efc8a2d18c965 |
completed | March 27, 2026, 6:52 p.m. |
Created at: March 27, 2026, 2:17 p.m.