Triple
T16768052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philipp |
E407519
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Felipe |
E466096
|
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: Felipe | Statement: [Philipp, hasCognate, Felipe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Felipe Context triple: [Philipp, hasCognate, Felipe]
-
A.
Felipe
chosen
Felipe is a Spanish-origin surname borne by various individuals, including the Filipino composer Julián Felipe.
-
B.
Felipe de Neve
Felipe de Neve was an 18th-century Spanish colonial governor of California best known for establishing the city of Los Angeles.
-
C.
Felipe Ángeles
Felipe Ángeles was a prominent Mexican military general and revolutionary figure who played a key role during the Mexican Revolution in the early 20th century.
-
D.
Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
-
E.
Fernando
Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b033d6b88190a1366a58d63b0546 |
completed | April 18, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00b283a9808190ba76110cf3c7f3a9 |
completed | May 10, 2026, 4:29 p.m. |
Created at: April 10, 2026, 5:21 a.m.