Triple
T16786940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Market Rasen |
E408003
|
entity |
| Predicate | hasPostTown |
P2711
|
FINISHED |
| Object | MARKET RASEN |
E408003
|
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: MARKET RASEN | Statement: [Market Rasen, hasPostTown, MARKET RASEN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MARKET RASEN Context triple: [Market Rasen, hasPostTown, MARKET RASEN]
-
A.
Market Rasen
chosen
Market Rasen is a small market town in Lincolnshire, England, best known for its historic town center and prominent National Hunt racecourse.
-
B.
Marktoffingen
Marktoffingen is a small municipality in the Donau-Ries district of Bavaria in southern Germany.
-
C.
ARKET
ARKET is a modern, minimalist lifestyle brand offering clothing, accessories, and homeware under the H&M Group.
-
D.
Markec
Markec is a South Slavic diminutive form of the male given name Marko, used as an affectionate or familiar nickname.
-
E.
Obor Market
Obor Market is one of Bucharest’s largest and oldest traditional markets, known for its wide variety of fresh produce, food stalls, and local goods.
- 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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b21b6a408190b0766138cc9a9ee2 |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00ab07bdcc819086479178e5b8b679 |
completed | May 10, 2026, 3:57 p.m. |
Created at: April 10, 2026, 5:22 a.m.