Triple
T1052329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chair W |
E22726
|
entity |
| Predicate | correspondsToLetter |
P17387
|
FINISHED |
| Object | W |
—
|
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: W | Statement: [Chair W, correspondsToLetter, W]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: correspondsToLetter Context triple: [Chair W, correspondsToLetter, W]
-
A.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another entity.
-
B.
hasLetter
chosen
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
C.
followsLetter
Indicates that one element in a sequence comes immediately after another element in alphabetical or ordered letter arrangement.
-
D.
letterSequence
Indicates that one sequence of letters directly follows or is ordered in relation to another within a larger string or alphabetic arrangement.
-
E.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
- 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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8b5312081909796df58fa7c1e9d |
completed | March 1, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69a4b7309cc481908ed839b0b8d75dbf |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.