Triple
T16751571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hato Rey |
E407092
|
entity |
| Predicate | hasNickName |
P39
|
FINISHED |
| Object |
Milla de Oro
Milla de Oro is the main financial and commercial district of San Juan, Puerto Rico, known for its concentration of banks, corporate offices, and upscale developments.
|
E1232138
|
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: Milla de Oro | Statement: [Hato Rey, hasNickName, Milla de Oro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Milla de Oro Context triple: [Hato Rey, hasNickName, Milla de Oro]
-
A.
The Oscar
The Oscar is a 1966 American drama film about the ruthless rise and moral downfall of a Hollywood actor, noted for its melodramatic portrayal of the film industry.
-
B.
Golden Lion
The Golden Lion is the top prize awarded for the best film at the prestigious Venice Film Festival.
-
C.
Goldener Bär
Goldener Bär is the German name for the Golden Bear, the top prize awarded for the best film at the Berlin International Film Festival.
-
D.
Rose d'Or
The Rose d'Or is a prestigious international entertainment award recognizing excellence in television and radio programming.
-
E.
Palme d’Or
The Palme d’Or is the highest prize awarded at the Cannes Film Festival, recognizing the best feature film in the festival’s official selection.
- 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: Milla de Oro Triple: [Hato Rey, hasNickName, Milla de Oro]
Generated description
Milla de Oro is the main financial and commercial district of San Juan, Puerto Rico, known for its concentration of banks, corporate offices, and upscale developments.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Milla de Oro Target entity description: Milla de Oro is the main financial and commercial district of San Juan, Puerto Rico, known for its concentration of banks, corporate offices, and upscale developments.
-
A.
The Oscar
The Oscar is a 1966 American drama film about the ruthless rise and moral downfall of a Hollywood actor, noted for its melodramatic portrayal of the film industry.
-
B.
Golden Lion
The Golden Lion is the top prize awarded for the best film at the prestigious Venice Film Festival.
-
C.
Goldener Bär
Goldener Bär is the German name for the Golden Bear, the top prize awarded for the best film at the Berlin International Film Festival.
-
D.
Rose d'Or
The Rose d'Or is a prestigious international entertainment award recognizing excellence in television and radio programming.
-
E.
Palme d’Or
The Palme d’Or is the highest prize awarded at the Cannes Film Festival, recognizing the best feature film in the festival’s official selection.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa271de48190b4a535408aeef734 |
completed | April 18, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a52402848190b029cb0be31b4c74 |
completed | May 10, 2026, 3:32 p.m. |
| NEDg | Description generation | batch_6a00a5c4e934819088db49d81be154c3 |
completed | May 10, 2026, 3:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00a6b84d288190aeccb06745146b80 |
completed | May 10, 2026, 3:39 p.m. |
Created at: April 10, 2026, 5:21 a.m.