Triple
T8831536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fred and Ginger |
E210155
|
entity |
| Predicate | hasTwinTowersMetaphor |
P84899
|
FINISHED |
| Object | dancers |
—
|
LITERAL 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: dancers | Statement: [Fred and Ginger, hasTwinTowersMetaphor, dancers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTwinTowersMetaphor Context triple: [Fred and Ginger, hasTwinTowersMetaphor, dancers]
-
A.
hasTwinTowers
Indicates that an entity possesses or is characterized by a pair of closely associated, similarly structured towers.
-
B.
hasArchitecturalTwin
Indicates that two entities share nearly identical architectural design, form, or structure, effectively making them architectural counterparts or duplicates.
-
C.
hasTwinCityStructure
Indicates that one city has an officially recognized twin-city (sister-city) relationship structure with another city.
-
D.
hasTwinCities
Indicates that two cities are officially recognized as twin (or sister) cities, typically based on cultural, economic, or historical partnership agreements.
-
E.
One World Trade CenterUse
Indicates that an entity makes use of, occupies, or operates within One World Trade Center.
- F. None of above. chosen
Provenance (4 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_69ca8365b28081909e48e45e95dfc405 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc604ed2b88190b4f53b34b5a438f7 |
completed | April 1, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69cc5c23d08481908d8c9b0ad3d1dc00 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cff3608819081d2d7e5c16d44b7 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:47 p.m.