Triple
T22787677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vélizy-Villacoublay |
E564013
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Harlow |
—
|
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: Harlow | Statement: [Vélizy-Villacoublay, hasTwinTown, Harlow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harlow Context triple: [Vélizy-Villacoublay, hasTwinTown, Harlow]
-
A.
Harlow
Harlow is a 1965 biographical drama film about the life and career of Hollywood actress Jean Harlow.
-
B.
Harlow
chosen
Harlow is a town in Essex, England, known as a post-war New Town with significant residential, commercial, and industrial development.
-
C.
Nutwood
Nutwood is the idyllic English village that serves as the primary setting for the classic British children's comic strip and stories about Rupert Bear.
-
D.
Peabody
Peabody is a suburban city in northeastern Massachusetts known for its location on the North Shore and its historical ties to the leather industry.
-
E.
Peabody
Peabody is the middle name of the American ethnologist and linguist J. P. Harrington, known for his extensive documentation of Native American languages and cultures.
- 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_69e2455500788190b4b33030461f3bbd |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17c32de6481909ef358d16de98496 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 3:29 p.m.