Triple
T15025489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Quaggy |
E378201
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Lee |
E392982
|
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: Lee | Statement: [River Quaggy, passesThrough, Lee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lee Context triple: [River Quaggy, passesThrough, Lee]
-
A.
Lee
Lee is a given name shared by numerous individuals across different cultures and professions.
-
B.
Lee
chosen
Lee is a residential district in southeast London known for its suburban character, green spaces, and Victorian and Edwardian housing.
-
C.
LEE
LEE is the stock ticker symbol for Lee Enterprises, a U.S.-based media company that owns and operates numerous local newspapers and digital news platforms.
-
D.
LEE
LEE is a robotic grappling mechanism used on spacecraft robotic arms, notably on the Canadarm systems, to securely latch onto fixtures and manipulate payloads in space.
-
E.
Lou
Lou is a supporting character in the romantic drama film "Stuck in Love," involved in the intertwined relationships and personal struggles of a family of writers.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7dfcb508190aec8cd667e27a8ea |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd499108190b803c6afc0fa00bc |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 2:57 a.m.