Triple
T3372602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew |
E70988
|
entity |
| Predicate | hasFeminineForm |
P1613
|
FINISHED |
| Object | Mattea |
E327488
|
NE 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: Mattea | Statement: [Matthew, hasFeminineForm, Mattea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mattea Context triple: [Matthew, hasFeminineForm, Mattea]
-
A.
Mattea
chosen
Mattea is a feminine given name of Italian origin, often interpreted to mean "gift of God" as a variant of Matteo/Matthew.
-
B.
Nena
Nena is a German pop singer and actress best known internationally for her 1983 hit song "99 Luftballons."
-
C.
Julanne
Julanne is a feminine given name most notably borne by American silent film actress Julanne Johnston.
-
D.
Naoma
Naoma is an unincorporated community and coal-mining town located in Raleigh County, West Virginia, United States.
-
E.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2bdcf70819087fc7e00fbd61e0d |
completed | March 8, 2026, 5:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3343fd8a08190bf426884ec42948c |
completed | March 12, 2026, 9:46 p.m. |
Created at: March 8, 2026, 3:13 p.m.