Triple
T5878246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Post Tower |
E130678
|
entity |
| Predicate | mainContractor |
P7138
|
FINISHED |
| Object | Hochtief |
E261848
|
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: Hochtief | Statement: [Post Tower, mainContractor, Hochtief]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hochtief Context triple: [Post Tower, mainContractor, Hochtief]
-
A.
Hochtief
chosen
Hochtief is a major German-based global construction group known for large-scale infrastructure, engineering, and building projects worldwide.
-
B.
BESIX
BESIX is a major Belgian construction and engineering company known for delivering large-scale, high-profile projects worldwide.
-
C.
Skanska AB
Skanska AB is a multinational Swedish construction and project development company known for large-scale infrastructure, commercial, and residential projects worldwide.
-
D.
Eiffage
Eiffage is a major French construction and civil engineering company known for delivering large-scale infrastructure projects such as the Millau Viaduct.
-
E.
ThyssenKrupp AG
ThyssenKrupp AG is a major German multinational conglomerate specializing in industrial engineering and steel production, with significant operations in areas such as elevators, automotive components, and plant technology.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036327efc8190858e9364cd5d317b |
completed | March 22, 2026, 6:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b12861c081909f95f1ef6a1f457c |
completed | March 23, 2026, 3:19 a.m. |
Created at: March 22, 2026, 3:57 p.m.