Triple
T15578294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nira Park |
E374424
|
entity |
| Predicate | produced |
P490
|
FINISHED |
| Object | Black Books |
E644705
|
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: Black Books | Statement: [Nira Park, produced, Black Books]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Black Books Context triple: [Nira Park, produced, Black Books]
-
A.
Black Books
chosen
Black Books is a British sitcom centered on the misanthropic owner of a chaotic London bookshop, known for its dark humor and cult following.
-
B.
The Brown Book
The Brown Book is one of Ludwig Wittgenstein’s posthumously published lecture notes that outline his transitional philosophical ideas between the Tractatus and his later work in the Philosophical Investigations.
-
C.
The Black Book
The Black Book is a film featuring Portuguese actress Daniela Melchior in its cast.
-
D.
The Black Book
The Black Book is a crime novel in Ian Rankin’s Inspector Rebus series, known for its dark Edinburgh setting and intricate, long-buried secrets.
-
E.
The Black Book
The Black Book is a postmodern novel by Turkish author Orhan Pamuk that blends mystery, philosophical reflection, and Istanbul’s labyrinthine atmosphere to explore identity and storytelling.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e22c89081909b1ec0cd36a1ef45 |
completed | April 16, 2026, 2:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4b3a8881909d41204a0b243461 |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:11 a.m.