Triple
T23427528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dov Hoenig |
E563233
|
entity |
| Predicate | edited |
P1932
|
FINISHED |
| Object | Heat |
E174842
|
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: Heat | Statement: [Dov Hoenig, edited, Heat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heat Context triple: [Dov Hoenig, edited, Heat]
-
A.
Heat
chosen
Heat is a 1995 crime thriller film directed by Michael Mann, renowned for its intense heist sequences and the iconic pairing of Al Pacino and Robert De Niro.
-
B.
Heat
Heat is a chapter or section within the novel "Like Water for Chocolate" that focuses on themes of passion, desire, and emotional intensity.
-
C.
Heat
Heat is OpenStack’s orchestration service that automates the deployment and management of cloud infrastructure using template-based definitions.
-
D.
Heat
Heat is a 1963 Soviet drama film directed by Larisa Shepitko, marking her acclaimed feature-length directorial debut.
-
E.
Heat
"Heat" is a non-fiction book by British explorer Ranulph Fiennes that recounts his extreme expeditions and experiences in some of the world's hottest and most hostile environments.
- 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_69e24553980c8190bb66a2ae0bdab125 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a54ae8ac8190b0bae2560582c904 |
completed | April 29, 2026, 6:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c67a7b9d88190897588a9ec4fbd2a |
completed | May 19, 2026, 1:37 p.m. |
Created at: April 17, 2026, 5:48 p.m.