Triple
T28302703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris La Défense towers projects |
E713750
|
entity |
| Predicate | notableTower |
P34309
|
FINISHED |
| Object |
Tour Sisters
Tour Sisters is a planned pair of modern high-rise office towers in the La Défense business district of Paris, designed as part of the area’s ongoing urban redevelopment.
|
E1810481
|
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: Tour Sisters | Statement: [Paris La Défense towers projects, notableTower, Tour Sisters]
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: Tour Sisters Triple: [Paris La Défense towers projects, notableTower, Tour Sisters]
Generated description
Tour Sisters is a planned pair of modern high-rise office towers in the La Défense business district of Paris, designed as part of the area’s ongoing urban redevelopment.
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_69efb524ab688190a1ce7ee7c9520932 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f644b3c6088190b3a20e8916fcddba |
completed | May 2, 2026, 6:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a160734fc0081909295271f22c6a046 |
completed | May 26, 2026, 8:48 p.m. |
| NEDg | Description generation | batch_6a161379293881908968fb41078efd89 |
completed | May 26, 2026, 9:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1613d98bbc8190816b8b1dce53e9ef |
completed | May 26, 2026, 9:42 p.m. |
Created at: April 27, 2026, 11:36 p.m.