Triple
T8318705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Compendious Dictionary of the English Language |
E194772
|
entity |
| Predicate | hasApproximateEntryCount |
P81913
|
FINISHED |
| Object | 37000 |
—
|
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: 37000 | Statement: [A Compendious Dictionary of the English Language, hasApproximateEntryCount, 37000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateEntryCount Context triple: [A Compendious Dictionary of the English Language, hasApproximateEntryCount, 37000]
-
A.
hasApproximateMemberCount
Indicates that an entity is associated with a group or collection for which only an estimated or non-exact number of members is known.
-
B.
hasApproximateBrickCount
Indicates that an entity is associated with an estimated or non-exact number of bricks.
-
C.
hasApproximateLeaves
Indicates that one entity possesses a number of leaves that is approximately equal to the number of leaves of another entity.
-
D.
hasApproximateValue
Indicates that one entity’s value is close to, but not exactly equal to, the value of another entity within an acceptable margin of error.
-
E.
hasApproximateNumberOfColumns
Indicates that an entity is associated with an estimated or non-exact count of columns.
- 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_69ca82e6e2648190a31eaf6f4f757b2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f648e10819081ad1fed870b2b86 |
completed | March 31, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69cb70bf689c8190a9d9b6b872abf53d |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb77690720819099de1e22b84a9563 |
completed | March 31, 2026, 7:27 a.m. |
Created at: March 30, 2026, 5:55 p.m.