Triple
T39454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woodstock, Oxfordshire |
E780
|
entity |
| Predicate | hasMarket |
P2714
|
FINISHED |
| Object | historic town market |
—
|
LITERAL 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: historic town market | Statement: [Woodstock, Oxfordshire, hasMarket, historic town market]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMarket Context triple: [Woodstock, Oxfordshire, hasMarket, historic town market]
-
A.
market
Indicates the act of promoting, advertising, or selling a product, service, or idea to potential buyers or target audiences.
-
B.
hasStockExchange
Indicates that an entity is associated with or listed on a particular stock exchange.
-
C.
hasMarker
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
-
D.
hasCountry
Indicates that one entity possesses, is associated with, or is located within a specific country.
-
E.
hasPartner
Indicates that one entity is in a partner relationship (such as romantic, life, or business partnership) with another entity.
- F. None of above. chosen
Provenance (4 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24b80f4a8819090d2bffe29824b90 |
completed | Feb. 28, 2026, 1:57 a.m. |
| PD | Predicate disambiguation | batch_69a24ab74c548190a54872e15c8394c3 |
completed | Feb. 28, 2026, 1:53 a.m. |
| PDg | Predicate description generation | batch_69a24b7fd2c08190a0057fe7aec6a1ee |
completed | Feb. 28, 2026, 1:57 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.