Triple
T16316782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amber Riley |
E396190
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Amber |
E458626
|
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: Amber | Statement: [Amber Riley, givenName, Amber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amber Context triple: [Amber Riley, givenName, Amber]
-
A.
Amber
Amber is a historic town near Jaipur in Rajasthan, India, renowned for its hilltop Amber Fort and rich Rajput architectural heritage.
-
B.
Amber
chosen
Amber is a feminine given name derived from the English word for the fossilized tree resin, often associated with a warm, golden color.
-
C.
Amber
Amber is a character from the film "Green Room," a tense horror-thriller about a punk band trapped in a remote venue controlled by violent neo-Nazis.
-
D.
Onyx
Onyx is a hardcore hip hop group from Queens, New York, known for its aggressive style and influential 1990s releases.
-
E.
Onyx
Onyx is a small unincorporated community in Kern County, California, located in the southern Sierra Nevada near Lake Isabella.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e296b1e9988190a1dce9f1ed7031df |
completed | April 17, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a002606f0e0819081f50dae9e6b7a0d |
completed | May 10, 2026, 6:30 a.m. |
Created at: April 10, 2026, 5:06 a.m.