Triple
T8899293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How Wood railway station |
E211884
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | How Wood |
E211884
|
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: How Wood | Statement: [How Wood railway station, locatedIn, How Wood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: How Wood Context triple: [How Wood railway station, locatedIn, How Wood]
-
A.
How Wood
chosen
How Wood is a residential suburb and railway-served locality near St Albans in Hertfordshire, England.
-
B.
De Wood
De Wood is a specific variant or form of wood distinguished from the general category of wood materials.
-
C.
Mine Woods
Mine Woods is a woodland park and popular recreational area near Bridge of Allan in central Scotland, known for its walking trails, wildlife, and scenic views.
-
D.
Rubio Woods
Rubio Woods is a forest preserve in Cook County, Illinois, known for its dense woodland and proximity to the famously haunted Bachelor's Grove Cemetery.
-
E.
Oaken
Oaken is a friendly shopkeeper and sauna owner from Disney's Frozen franchise, known for his cheerful demeanor and memorable "Yoo-hoo!" greeting.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64278b208190afc3dec64ecdb0f5 |
completed | April 1, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfba212fd081909ae87853c81e1d30 |
completed | April 3, 2026, 1:01 p.m. |
Created at: March 30, 2026, 6:54 p.m.