Triple
T20134138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joshua R. Giddings |
E490976
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Joshua |
—
|
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: Joshua | Statement: [Joshua R. Giddings, givenName, Joshua]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joshua Context triple: [Joshua R. Giddings, givenName, Joshua]
-
A.
Joshua
Joshua is a book of the Hebrew Bible and Christian Old Testament that narrates the Israelite conquest and settlement of Canaan under the leadership of Joshua.
-
B.
Joshua
chosen
Joshua is a masculine given name of Hebrew origin, commonly used in English-speaking countries.
-
C.
Joshua
Joshua is a central biblical leader who succeeded Moses, led the Israelites into the Promised Land, and is the namesake of the Book of Joshua in the Hebrew Bible.
-
D.
Joshua
Joshua is a ruthless and highly skilled mercenary who serves as the primary henchman antagonist in the action film "Lethal Weapon."
-
E.
Yoshua
Yoshua is a male given name most notably borne by Yoshua Bengio, a pioneering Canadian computer scientist and deep learning researcher.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6676556c481909ffc80cd2009b65a |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.