Triple
T274971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thames |
E5226
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Reading |
E22663
|
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: Reading | Statement: [Thames, flowsThrough, Reading]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reading Context triple: [Thames, flowsThrough, Reading]
-
A.
Reading
chosen
Reading is a major town in Berkshire, England, known as a key commercial and transport hub in the Thames Valley.
-
B.
Goodreads
Goodreads is a popular social cataloging website where readers track, rate, review, and discover books and reading recommendations.
-
C.
Writings
Writings is the third major section of the Hebrew Bible, comprising a diverse collection of poetic, wisdom, and historical books such as Psalms, Proverbs, and Job.
-
D.
Pan Books
Pan Books is a British publishing imprint known for producing popular fiction and classic titles, including major science fiction works.
-
E.
Reedus
Reedus is the surname of American actor and model Norman Reedus, best known for his role as Daryl Dixon on the television series "The Walking Dead."
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25dd1cdf881909c2c9b77b7f88684 |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a391506e2881909fa399aab00d3ac9 |
completed | March 1, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.