Triple
T17681564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Extended Essay |
E440782
|
entity |
| Predicate | maximumWordCount |
P7605
|
FINISHED |
| Object | 4000 words |
—
|
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: 4000 words | Statement: [Extended Essay, maximumWordCount, 4000 words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumWordCount Context triple: [Extended Essay, maximumWordCount, 4000 words]
-
A.
maximumTermCount
Indicates the highest number of terms that are allowed or considered within a given context or operation.
-
B.
wordCount
chosen
Indicates the total number of words contained in a given text or linguistic unit.
-
C.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
D.
maximumNumber
Indicates that one entity specifies the highest allowable or observed quantity, value, or count associated with another entity.
-
E.
maximumFrequency
Indicates the highest number of times a particular event, value, or occurrence appears within a given set or context.
- F. None of above.
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_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. |
Created at: April 10, 2026, 10:01 a.m.