Triple
T7809467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hot Water |
E180640
|
entity |
| Predicate | distributor |
P1951
|
FINISHED |
| Object | Pathé Exchange |
E709716
|
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: Pathé Exchange | Statement: [Hot Water, distributor, Pathé Exchange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pathé Exchange Context triple: [Hot Water, distributor, Pathé Exchange]
-
A.
Pathé Exchange
chosen
Pathé Exchange was an early 20th-century American film distribution company known for handling and releasing numerous silent and early sound films.
-
B.
Pathé
Pathé is a historic French film production and distribution company that also operated as a major record label in the early and mid-20th century.
-
C.
Gaumont cinemas
Gaumont cinemas is a historic French cinema chain known for operating movie theaters across France and being one of the oldest names in the film exhibition industry.
-
D.
Cineplex Cinemas
Cineplex Cinemas is a major Canadian movie theatre chain offering multiplex cinema experiences with multiple screens, concessions, and modern film presentation technologies.
-
E.
Wanda Cinemas
Wanda Cinemas is a major Chinese cinema chain known for operating a large network of modern movie theaters across China.
- 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78bb4b08190b2b3b51c5a0a033c |
completed | March 30, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc9336e14c8190ad925da158d98596 |
completed | April 1, 2026, 3:38 a.m. |
Created at: March 30, 2026, 4:36 p.m.