Triple
T7529999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Cats and the Woman They Own |
E177994
|
entity |
| Predicate | titleCharacterCount |
P32078
|
FINISHED |
| Object | three |
—
|
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: three | Statement: [Two Cats and the Woman They Own, titleCharacterCount, three]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: titleCharacterCount Context triple: [Two Cats and the Woman They Own, titleCharacterCount, three]
-
A.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
B.
numberOfCharacters
chosen
Indicates the total count of individual characters present in a given text, string, or entity’s representation.
-
C.
titleCharacterString
Indicates that one entity is the textual string representing the title associated with another entity.
-
D.
titleCount
Indicates the number of distinct titles associated with an entity within a given context.
-
E.
textCharacter
Indicates that one entity is a character (such as a letter, digit, or symbol) within a piece of text associated with another entity.
- 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_69c69f2acdbc8190b5a8320168c1d0ba |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81fbd4c8190b8ffedf1dbbb43aa |
completed | March 27, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d6bb808190bdd04499fd3bceb6 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:47 p.m.