Triple
T34565719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "Where's the rest of me?" |
E887472
|
entity |
| Predicate | approximateLengthInWords |
P7605
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: ["Where's the rest of me?", approximateLengthInWords, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateLengthInWords Context triple: ["Where's the rest of me?", approximateLengthInWords, 5]
-
A.
lengthInWords
Indicates the number of words that make up the length of something, typically a text or expression.
-
B.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
C.
wordLength
Indicates that there is a relationship specifying the number of characters (length) in a given word.
-
D.
wordCount
chosen
Indicates the total number of words contained in a given text or linguistic unit.
-
E.
lengthInArabicWordsApprox
Indicates that the approximate length of something is expressed in Arabic words.
- 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_69f349d0c4d881908dd0950f5eb9ec0a |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff234f32888190a1d800a3bda432eb |
completed | May 9, 2026, 12:06 p.m. |
| PD | Predicate disambiguation | batch_69ff228ae9a0819083f4b97c10b923f4 |
completed | May 9, 2026, 12:03 p.m. |
Created at: May 1, 2026, 2:02 a.m.