Triple
T36451458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M-I-Z Z-O-U chant |
E898021
|
entity |
| Predicate | usesLettersFrom |
P185516
|
FINISHED |
| Object | Mizzou |
—
|
NE NERFINISHED |
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: Mizzou | Statement: [M-I-Z Z-O-U chant, usesLettersFrom, Mizzou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesLettersFrom Context triple: [M-I-Z Z-O-U chant, usesLettersFrom, Mizzou]
-
A.
usesAdditionalLettersFrom
Indicates that one entity forms or derives its representation by incorporating extra letters taken from another entity beyond those originally present.
-
B.
hasLettersFor
Indicates that one entity possesses or contains written correspondence intended for another entity.
-
C.
usesAlphabet
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
-
D.
usesSpecialLetterFor
Indicates that one entity employs a particular special letter or character specifically in relation to another entity.
-
E.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
- 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_69f76e57f08481908593bd0bc34581c8 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bccf05bc8190b61fdb2b2a315811 |
completed | May 3, 2026, 9:23 p.m. |
| PDg | Predicate description generation | batch_69f7be9b9ab481908328e0e8d8ac73d4 |
completed | May 3, 2026, 9:31 p.m. |
Created at: May 3, 2026, 4:10 p.m.