Triple
T16768053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philipp |
E407519
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Filip |
E646188
|
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: Filip | Statement: [Philipp, hasCognate, Filip]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Filip Context triple: [Philipp, hasCognate, Filip]
-
A.
Filip
chosen
Filip is a masculine given name, commonly used in various European countries, that is a variant of the name Philip.
-
B.
Filipów
Filipów is a small town in northeastern Poland, known for its picturesque lakes and rural landscapes.
-
C.
Philippine
Philippine is a feminine given name of French origin historically borne by European nobility and royalty.
-
D.
Palaw
Palaw is a town located in Myanmar’s southern Tanintharyi Region, known for its coastal setting along the Andaman Sea and its role as a local administrative and trading center.
-
E.
Philippines
The Philippines is a Southeast Asian archipelagic country in the western Pacific Ocean known for its diverse culture, colonial history, and thousands of islands.
- 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_6a00a533e83481909966a7b86c8c8e64 |
completed | May 10, 2026, 3:33 p.m. |
Created at: April 10, 2026, 5:21 a.m.