Triple
T6077823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston waterfront |
E135445
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Fan Pier
Fan Pier is a redeveloped mixed-use district on Boston’s waterfront known for its modern offices, luxury residences, public parks, and harborfront views.
|
E567593
|
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: Fan Pier | Statement: [Boston waterfront, contains, Fan Pier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fan Pier Context triple: [Boston waterfront, contains, Fan Pier]
-
A.
The Pier
The Pier is a Spanish mystery drama television series that follows an architect investigating her late husband's double life on the Valencian coast.
-
B.
H Pier
H Pier is one of the passenger boarding concourses at Amsterdam Airport Schiphol, serving multiple gates for international flights.
-
C.
G Pier
G Pier is one of the passenger boarding concourses at Amsterdam Airport Schiphol, serving multiple gates for international flights.
-
D.
Town Pier
Town Pier is a historic riverside pier in Gravesend, England, serving as a local landmark and passenger landing stage on the River Thames.
-
E.
Pier B
Pier B is one of the main passenger boarding concourses at Brussels Airport, primarily serving non-Schengen and long-haul international flights.
- 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: Fan Pier Triple: [Boston waterfront, contains, Fan Pier]
Generated description
Fan Pier is a redeveloped mixed-use district on Boston’s waterfront known for its modern offices, luxury residences, public parks, and harborfront views.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fan Pier Target entity description: Fan Pier is a redeveloped mixed-use district on Boston’s waterfront known for its modern offices, luxury residences, public parks, and harborfront views.
-
A.
The Pier
The Pier is a Spanish mystery drama television series that follows an architect investigating her late husband's double life on the Valencian coast.
-
B.
H Pier
H Pier is one of the passenger boarding concourses at Amsterdam Airport Schiphol, serving multiple gates for international flights.
-
C.
G Pier
G Pier is one of the passenger boarding concourses at Amsterdam Airport Schiphol, serving multiple gates for international flights.
-
D.
Town Pier
Town Pier is a historic riverside pier in Gravesend, England, serving as a local landmark and passenger landing stage on the River Thames.
-
E.
Pier B
Pier B is one of the main passenger boarding concourses at Brussels Airport, primarily serving non-Schengen and long-haul international flights.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057706d9881909b52093282593886 |
completed | March 22, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d48f0508190991453dc17c53b89 |
completed | March 23, 2026, 11 a.m. |
| NEDg | Description generation | batch_69c11ed5a5748190b7320890397c8ce9 |
completed | March 23, 2026, 11:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c11f34218c819094ce32eafff37489 |
completed | March 23, 2026, 11:08 a.m. |
Created at: March 22, 2026, 4:11 p.m.