Triple
T8229602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linear B |
E192255
|
entity |
| Predicate | numberOfSyllabicSignsApprox |
P80978
|
FINISHED |
| Object | about 87 |
—
|
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: about 87 | Statement: [Linear B, numberOfSyllabicSignsApprox, about 87]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSyllabicSignsApprox Context triple: [Linear B, numberOfSyllabicSignsApprox, about 87]
-
A.
hasSyllableCount
Indicates that one entity (typically a word or phrase) possesses a specific number of syllables given by the other entity.
-
B.
usesSyllables
Indicates that one entity forms, expresses, or analyzes something by employing syllables as its basic units.
-
C.
hasSyllabicStructure
Indicates that an entity possesses a specific arrangement or pattern of syllables, such as their number, order, or type.
-
D.
hasNumberOfVowelSigns
Indicates the count of vowel signs associated with or present in a given linguistic unit (such as a character, syllable, or word).
-
E.
hasSyllabary
Indicates that one entity possesses or is associated with a specific syllabary writing system used to represent its language or notation.
- 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_69ca82db5b90819085d1ad7c2e27bfcc |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7802417c81908837c31136c41a5c |
completed | March 31, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69cb36b1dea0819091418072501e79c1 |
completed | March 31, 2026, 2:51 a.m. |
| PDg | Predicate description generation | batch_69cb3d6c34708190a987d68529cbb0b3 |
completed | March 31, 2026, 3:20 a.m. |
Created at: March 30, 2026, 5:46 p.m.