Triple
T17681590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Extended Essay |
E440782
|
entity |
| Predicate | wordCountExcludes |
P128551
|
FINISHED |
| Object | title page |
—
|
LITERAL 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: title page | Statement: [Extended Essay, wordCountExcludes, title page]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wordCountExcludes Context triple: [Extended Essay, wordCountExcludes, title page]
-
A.
wordCount
Indicates the total number of words contained in a given text or linguistic unit.
-
B.
excludesLetter
Indicates that one entity does not contain or allow the presence of a specified letter.
-
C.
writingSystemExcludes
Indicates that a writing system deliberately omits or does not represent certain elements (such as sounds, symbols, or features) that might otherwise be included.
-
D.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
E.
numberOfArticlesOmitted
Indicates the count of articles that have been left out or excluded from a given set, collection, or context.
- F. None of above. chosen
Provenance (4 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e470445b3881908bb0930b986089f7 |
completed | April 19, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:01 a.m.