Triple
T30600724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tengwar |
E778894
|
entity |
| Predicate | hasGlyphCount |
P4428
|
FINISHED |
| Object | around 24 primary consonant letters (tengwar) |
—
|
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: around 24 primary consonant letters (tengwar) | Statement: [Tengwar, hasGlyphCount, around 24 primary consonant letters (tengwar)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGlyphCount Context triple: [Tengwar, hasGlyphCount, around 24 primary consonant letters (tengwar)]
-
A.
hasGlyphsFor
Indicates that one entity provides or contains the necessary glyphs or visual symbols to represent another entity.
-
B.
hasGlyphRepertoireSize
chosen
Indicates the number of distinct glyphs included in an entity’s glyph repertoire.
-
C.
hasNumberOfBasicCharacters
Indicates the quantity of basic (non-accented or fundamental) characters associated with an entity.
-
D.
hasStrokeCount
Indicates the number of strokes required to write a given symbol or character.
-
E.
hasStrokeCountApprox
Indicates an approximate number of strokes associated with writing or drawing the related 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_69f224a1570c8190a85d3ac330479a79 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:25 p.m.