Triple
T21804327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M4 corridor |
E538311
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Reading |
—
|
NE NERFINISHED |
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: [M4 corridor, hasPart, Reading]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Reading Context triple: [M4 corridor, hasPart, 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.
Reading
Reading is a historic city in southeastern Pennsylvania known for its industrial heritage, transportation links, and role as a regional cultural and economic center.
-
C.
Reading
"Reading" is an Impressionist painting by Berthe Morisot that depicts a quiet, intimate moment of a woman absorbed in a book.
-
D.
Lectura
Lectura is an earlier version of John Duns Scotus’s theological commentary on Peter Lombard’s Sentences, preceding and later revised into the more polished Ordinatio.
-
E.
Read
Read is a village in Lancashire, England, situated near the River Calder and known for its residential community and local amenities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c4733f4081909a86622e7e6d15d2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f07801fa8081909098d00947bf8f7f |
completed | April 28, 2026, 9:04 a.m. |
Created at: April 16, 2026, 6:53 p.m.