Triple
T7326147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rayleigh |
E168879
|
entity |
| Predicate | postalTown |
P2711
|
FINISHED |
| Object | RAYLEIGH |
E168879
|
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: RAYLEIGH | Statement: [Rayleigh, postalTown, RAYLEIGH]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RAYLEIGH Context triple: [Rayleigh, postalTown, RAYLEIGH]
-
A.
Rayleigh
chosen
Rayleigh is a historic market town in the county of Essex, England, known for its medieval roots and prominent hilltop location.
-
B.
Ramsey
Ramsey is a coastal town in the north of the Isle of Man, known as one of the island’s main population centers and a local commercial and transport hub.
-
C.
Ramsey
Ramsey is a brilliant hacker and tech expert in the Fast & Furious film series, known for creating the powerful surveillance program "God's Eye."
-
D.
Ramsey
Ramsey is a historic market town in the English county of Cambridgeshire, known for its medieval abbey and rural surroundings.
-
E.
Ramsey
Ramsey is a surname of English and Scottish origin borne by various notable individuals across fields such as science, politics, and the arts.
- 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_69c68a54cacc81908e3b773441f19566 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f0a612c08190b7a3fefa811bbcec |
completed | March 27, 2026, 9:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7ef0e3e7481908e6cedbd3f0077ca |
completed | March 28, 2026, 3:09 p.m. |
Created at: March 27, 2026, 3:03 p.m.