Triple
T17257792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Rafa |
E418927
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object |
Rafa
Rafa is a town in the southern Gaza Strip, near the border with Egypt, known historically as the site of several military engagements.
|
E1259461
|
NE FINISHED |
How this triple was built (4 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: Rafa | Statement: [Battle of Rafa, location, Rafa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rafa Context triple: [Battle of Rafa, location, Rafa]
-
A.
Rafa Pabön
Rafa Pabön is a Puerto Rican urban Latin singer and songwriter known for his reggaeton and Latin trap hits and collaborations across the Latin music scene.
-
B.
Rafael
Rafael is a masculine given name of Hebrew origin, commonly used in Spanish, Portuguese, and other languages, meaning "God has healed."
-
C.
Feliciano
Feliciano is a given name of Latin origin, commonly used in Romance-language countries and related to the name Felix.
-
D.
Rosario Nadal
Rosario Nadal is a Spanish former model and art consultant known for her marriage into the former Bulgarian royal family as the wife of Prince Kyril of Bulgaria.
-
E.
Rubén
Rubén is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rafa Triple: [Battle of Rafa, location, Rafa]
Generated description
Rafa is a town in the southern Gaza Strip, near the border with Egypt, known historically as the site of several military engagements.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rafa Target entity description: Rafa is a town in the southern Gaza Strip, near the border with Egypt, known historically as the site of several military engagements.
-
A.
Rafa Pabön
Rafa Pabön is a Puerto Rican urban Latin singer and songwriter known for his reggaeton and Latin trap hits and collaborations across the Latin music scene.
-
B.
Rafael
Rafael is a masculine given name of Hebrew origin, commonly used in Spanish, Portuguese, and other languages, meaning "God has healed."
-
C.
Feliciano
Feliciano is a given name of Latin origin, commonly used in Romance-language countries and related to the name Felix.
-
D.
Rosario Nadal
Rosario Nadal is a Spanish former model and art consultant known for her marriage into the former Bulgarian royal family as the wife of Prince Kyril of Bulgaria.
-
E.
Rubén
Rubén is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
- F. None of above. chosen
Provenance (5 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e6dde4881908e7fc01fd5364616 |
completed | April 19, 2026, 1:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170ff6818819090077dc4a7b774ae |
completed | May 11, 2026, 6:02 a.m. |
| NEDg | Description generation | batch_6a017521c90c819099cea67e4084aa67 |
completed | May 11, 2026, 6:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01760409ac8190ac7714e31e686d9a |
completed | May 11, 2026, 6:24 a.m. |
Created at: April 10, 2026, 5:39 a.m.