Triple
T35779548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Classical Dictionary of the Vulgar Tongue |
E1034397
|
entity |
| Predicate | hasApproximateEntries |
P81913
|
FINISHED |
| Object | over 900 |
—
|
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: over 900 | Statement: [A Classical Dictionary of the Vulgar Tongue, hasApproximateEntries, over 900]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateEntries Context triple: [A Classical Dictionary of the Vulgar Tongue, hasApproximateEntries, over 900]
-
A.
hasApproximateEntryCount
chosen
Indicates that an entity is associated with a number representing an estimated or non-exact count of its entries.
-
B.
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.
-
C.
hasApproximateStoreCount
Indicates that an entity is associated with an estimated or approximate number of stores, rather than an exact count.
-
D.
hasApproximateBrickCount
Indicates that an entity is associated with an estimated or non-exact number of bricks.
-
E.
hasKeyApprox
Indicates that one entity possesses a key that approximately matches, corresponds to, or can operate with another entity, but not necessarily with exact or perfect equivalence.
- 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_69f76e14a1e081908eddd57bd6fdb3be |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe031bc6208190860099aef72d8dcb |
completed | May 8, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69fe014c8b388190b5d4e0cb95ee2be5 |
completed | May 8, 2026, 3:29 p.m. |
Created at: May 3, 2026, 4:06 p.m.