Triple
T17126745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rukn al-Dawla |
E415615
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Ray |
E255763
|
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: Ray | Statement: [Rukn al-Dawla, capital, Ray]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Context triple: [Rukn al-Dawla, capital, Ray]
-
A.
Ray
chosen
Ray is an ancient city near modern-day Tehran in Iran that served as a significant political and cultural center in various Persian empires.
-
B.
Ray
Ray is an open-source distributed computing framework designed to scale Python applications for tasks like machine learning, reinforcement learning, and data processing across clusters.
-
C.
Ray
Ray is the protagonist of the novel "The Keep," around whom the story’s central psychological and narrative tensions revolve.
-
D.
Ray
Ray is the optimistic Cajun firefly from Disney’s *The Princess and the Frog*, known for his devotion to his love “Evangeline” and his role in aiding Tiana and Naveen.
-
E.
Ray
Ray is the romantic, Cajun firefly character from Disney’s animated film "The Princess and the Frog," known for his heartfelt song "Ma Belle Evangeline."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f027a3d081908fc1134b50db3d45 |
completed | April 18, 2026, 8:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01414a51d4819086c2346fe2d4fce4 |
completed | May 11, 2026, 2:39 a.m. |
Created at: April 10, 2026, 5:36 a.m.