Triple
T8315179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellis Boyd "Red" Redding |
E194687
|
entity |
| Predicate | hairColorOriginOfNickname |
P7596
|
FINISHED |
| Object | red hair (in novella) |
—
|
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: red hair (in novella) | Statement: [Ellis Boyd "Red" Redding, hairColorOriginOfNickname, red hair (in novella)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hairColorOriginOfNickname Context triple: [Ellis Boyd "Red" Redding, hairColorOriginOfNickname, red hair (in novella)]
-
A.
nameEtymologyFor
Indicates that one entity expresses or explains the origin or derivation of the name of another entity.
-
B.
hasNameOrigin
Indicates that the origin or source of an entity’s name is specified by the related entity.
-
C.
possibleNameEtymology
Indicates a hypothesized or suggested origin or derivation of an entity’s name from another term, source, or linguistic root.
-
D.
colorOrigin
Indicates the source or cause from which an entity’s color is derived or determined.
-
E.
reasonForNickname
chosen
Indicates the explanation or cause behind why a particular nickname was given to an 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_69ca82e6e2648190a31eaf6f4f757b2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f540b2081908ccb1b2ed040c74e |
completed | March 31, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69cb70bf689c8190a9d9b6b872abf53d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:55 p.m.