Triple
T2758813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orlando Hernández |
E61169
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Orlando |
E11265
|
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: Orlando | Statement: [Orlando Hernández, givenName, Orlando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orlando Context triple: [Orlando Hernández, givenName, Orlando]
-
A.
Orlando
chosen
Orlando is a major city in central Florida known for its theme parks, tourism industry, and entertainment attractions.
-
B.
Orlando
Orlando is a historic township area within Soweto, South Africa, known for its central role in the anti-apartheid struggle and vibrant local culture.
-
C.
West Palm Beach
West Palm Beach is a coastal city in South Florida known for its waterfront downtown, cultural attractions, and role as a major urban center in Palm Beach County.
-
D.
Jacksonville, Florida
Jacksonville, Florida is a major city in northeastern Florida known for its extensive riverfront, large land area, and role as a regional economic and transportation hub.
-
E.
Kissimmee, Florida
Kissimmee, Florida is a central Florida city in Osceola County known for its proximity to major Orlando-area theme parks and tourist attractions.
- 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb8d34f88190a760111fe303cf24 |
completed | March 7, 2026, 8:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1de83d3008190bfb483e0c1e75700 |
completed | March 11, 2026, 9:28 p.m. |
Created at: March 6, 2026, 9:57 p.m.