Triple
T179806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon & Schuster |
E3658
|
entity |
| Predicate | hasImprint |
P2763
|
FINISHED |
| Object |
Howard Books
Howard Books is a Christian and inspirational book publishing imprint of Simon & Schuster.
|
E23423
|
NE FINISHED |
How this triple was built (4 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: Howard Books | Statement: [Simon & Schuster, hasImprint, Howard Books]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Howard Books Context triple: [Simon & Schuster, hasImprint, Howard Books]
-
A.
Pan Books
Pan Books is a British publishing imprint known for producing popular fiction and classic titles, including major science fiction works.
-
B.
Pantheon Books
Pantheon Books is an American publishing imprint known for releasing influential works in politics, history, and critical theory.
-
C.
Metropolitan Books
Metropolitan Books is an American publishing imprint known for releasing influential and often politically engaged nonfiction works by prominent intellectuals and public thinkers.
-
D.
Warner Books
Warner Books was a major American publishing imprint known for releasing a wide range of popular fiction and nonfiction titles.
-
E.
Cassell
Cassell is a British publishing company known for producing a wide range of books, including notable historical works and reference titles.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Howard Books Triple: [Simon & Schuster, hasImprint, Howard Books]
Generated description
Howard Books is a Christian and inspirational book publishing imprint of Simon & Schuster.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Howard Books Target entity description: Howard Books is a Christian and inspirational book publishing imprint of Simon & Schuster.
-
A.
Pan Books
Pan Books is a British publishing imprint known for producing popular fiction and classic titles, including major science fiction works.
-
B.
Pantheon Books
Pantheon Books is an American publishing imprint known for releasing influential works in politics, history, and critical theory.
-
C.
Metropolitan Books
Metropolitan Books is an American publishing imprint known for releasing influential and often politically engaged nonfiction works by prominent intellectuals and public thinkers.
-
D.
Warner Books
Warner Books was a major American publishing imprint known for releasing a wide range of popular fiction and nonfiction titles.
-
E.
Cassell
Cassell is a British publishing company known for producing a wide range of books, including notable historical works and reference titles.
- F. None of above. chosen
Provenance (5 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_69a25374990081909766d30c79a18e0e |
completed | Feb. 28, 2026, 2:31 a.m. |
| NER | Named-entity recognition | batch_69a25bafd5808190a0a0cb2b21ce007f |
completed | Feb. 28, 2026, 3:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2fd0405608190bf4c1aa8a8fa9ad1 |
completed | Feb. 28, 2026, 2:34 p.m. |
| NEDg | Description generation | batch_69a2fe96bec48190a7b8030b466b5e62 |
completed | Feb. 28, 2026, 2:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2ff0f87b08190a5b4713d2b3340c6 |
completed | Feb. 28, 2026, 2:43 p.m. |
Created at: Feb. 28, 2026, 2:39 a.m.