Triple
T19532126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamming bound |
E488680
|
entity |
| Predicate | tightFor |
P136673
|
FINISHED |
| Object | perfect codes |
—
|
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: perfect codes | Statement: [Hamming bound, tightFor, perfect codes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tightFor Context triple: [Hamming bound, tightFor, perfect codes]
-
A.
tightens
Indicates that one entity makes another entity more secure, compact, or taut by applying constricting force or reducing looseness.
-
B.
tightEnd
Indicates a relationship where an entity plays the tight end position in (or is assigned the role of tight end within) a football team or formation.
-
C.
isTight
Indicates that one entity fits closely or securely around, against, or within another without looseness or extra space.
-
D.
tieInWith
Indicates that one thing is connected, coordinated, or made consistent with another, often as part of a combined plan, theme, or schedule.
-
E.
binding
Indicates that one entity physically or chemically attaches, adheres, or forms a stable association with another entity.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6363fd1f8819080805346efad2579 |
completed | April 20, 2026, 2:20 p.m. |
| PD | Predicate disambiguation | batch_69e514c9c00481909b76bda67957e58b |
completed | April 19, 2026, 5:45 p.m. |
| PDg | Predicate description generation | batch_69e51a23300c8190988552491d9783d7 |
completed | April 19, 2026, 6:08 p.m. |
Created at: April 10, 2026, 1:41 p.m.