Triple
T17237439
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Austin |
E418399
|
entity |
| Predicate | friendOf |
P8712
|
FINISHED |
| Object | Pablo |
E30439
|
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: Pablo | Statement: [Austin, friendOf, Pablo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pablo Context triple: [Austin, friendOf, Pablo]
-
A.
Pablo
chosen
Pablo is a given name, especially common in Spanish-speaking countries, that corresponds to the English name Paul.
-
B.
Pablo Francisco
Pablo Francisco is an American stand-up comedian known for his high-energy performances, rapid-fire impressions, and popular Comedy Central specials.
-
C.
Eduardo
Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
-
D.
Rufino
Rufino is a masculine given name, commonly used in Spanish and Portuguese, that is cognate with the Latin-derived name Rufus.
-
E.
Paco
Paco is a riverside district in Manila, Philippines, known for its historic sites, markets, and dense urban neighborhoods.
- 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_69d886d8e96081909870bff6c3d0bf09 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42dfcf2608190b6935b79ea2ae946 |
completed | April 19, 2026, 1:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01793efa288190b63f641381566bb9 |
completed | May 11, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:39 a.m.