Triple
T182689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rebecca |
E3910
|
entity |
| Predicate | hasSyllableCount |
P6574
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Rebecca, hasSyllableCount, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSyllableCount Context triple: [Rebecca, hasSyllableCount, 3]
-
A.
hasNumberOfVowelLetters
Indicates that an entity is associated with a specific count of vowel letters it contains.
-
B.
hasNumberOfConsonantLetters
Indicates the relationship between an entity and the count of consonant letters present in its written form.
-
C.
hasStressPattern
Indicates that an entity (such as a word or phrase) follows a particular arrangement of stressed and unstressed units (e.g., syllables) in its pronunciation.
-
D.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
E.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a2592516748190a85ae58eec191f14 |
completed | Feb. 28, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69a2566dfc388190988b1b42d5daaafe |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a257e763d081908c54ad57d8d3060d |
completed | Feb. 28, 2026, 2:50 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.