Triple
T13235883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johnnie Ray |
E315145
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ray |
E19092
|
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: [Johnnie Ray, familyName, Ray]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray Context triple: [Johnnie Ray, familyName, Ray]
-
A.
Ray
chosen
Ray is a masculine given name commonly used in English-speaking countries, often as a short form of Raymond.
-
B.
Ray
Ray is an ancient city near modern-day Tehran in Iran that served as a significant political and cultural center in various Persian empires.
-
C.
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.
-
D.
Ray
Ray is the central figure in Claude McKay’s novel "Home to Harlem," embodying the intellectual, conflicted perspective on Black identity and urban life during the Harlem Renaissance.
-
E.
Ray
Ray is the guilt-ridden hitman protagonist of the dark comedy crime film "In Bruges," portrayed by Colin Farrell.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d36bdf8819099949b1e0e6902d3 |
completed | April 10, 2026, 11:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ff3079c08190977663e5d4762a80 |
completed | May 3, 2026, 7:54 a.m. |
Created at: April 9, 2026, 9:22 p.m.