Triple
T11726504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tricia |
E278783
|
entity |
| Predicate | meaningViaPatricia |
P101009
|
FINISHED |
| Object | noblewoman |
—
|
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: noblewoman | Statement: [Tricia, meaningViaPatricia, noblewoman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meaningViaPatricia Context triple: [Tricia, meaningViaPatricia, noblewoman]
-
A.
meaningViaLeonard
Indicates that something has a particular meaning or interpretation as conveyed or defined by Leonard.
-
B.
letterMeaning
Indicates that a particular letter conveys a specific meaning, interpretation, or semantic content.
-
C.
telegraphMessageMeaning
Indicates that one entity conveys the semantic content or intended message of a telegraph sent between parties.
-
D.
stringMeaning
Indicates that one entity represents the semantic content or interpretation of a given string associated with another entity.
-
E.
lettersMeaning
Indicates that a set of letters or characters represents, signifies, or conveys a particular meaning or message.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4d70d908190b5f47c2ef501a191 |
completed | April 10, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69d88a7f51248190bf492bd7509b5413 |
completed | April 10, 2026, 5:28 a.m. |
| PDg | Predicate description generation | batch_69d890458d948190b15054c9ba0fd923 |
completed | April 10, 2026, 5:53 a.m. |
Created at: April 8, 2026, 9:41 p.m.