Triple
T3735536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kings River |
E79172
|
entity |
| Predicate | namedFor |
P63
|
FINISHED |
| Object | Kings |
E19168
|
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: Kings | Statement: [Kings River, namedFor, Kings]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kings Context triple: [Kings River, namedFor, Kings]
-
A.
Kings
chosen
Kings is a biblical book in the Hebrew Bible that narrates the history of the Israelite monarchies from Solomon through the fall of Jerusalem.
-
B.
The Royals
The Royals is the nickname of Reading Football Club, a professional English football team based in Reading, Berkshire.
-
C.
KING
KING is the stock ticker symbol for King Digital Entertainment, the video game company best known for creating the mobile puzzle game Candy Crush Saga.
-
D.
KING
KING is a television station in Seattle, Washington, known for its local news coverage and affiliation with major U.S. broadcast networks.
-
E.
King
King is a common English surname borne by numerous notable figures, including civil rights leader Martin Luther King Jr.
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb3b399c819091b42209925c0d8f |
completed | March 8, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db1be1388190a887d9eca0f4f9b3 |
completed | March 14, 2026, 3:50 a.m. |
Created at: March 8, 2026, 3:34 p.m.